Development and evaluation of mobile learning application for practical chemistry among pre-service teachers 

Development and evaluation of mobile learning application for practical chemistry among pre-service teachers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and evaluation of mobile learning application for practical chemistry among pre-service teachers Favour Mosunmola Sobowale, Ibrahim Abba Mohammed, Berechiah Manji Samson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5071560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Lack of proper laboratories and inadequate facilities have become some of the factors affecting the teaching of chemistry practical in Nigeria which therefore affects students’ performance. While there were many studies conducted on the effectiveness of mobile learning, literature remains very scarce in the Nigerian context. In order to tackle this problem, using ADDIE instructional design model, we developed and tested the effectiveness of mobile learning towards improving college of education pre-service teachers’ achievement in practical chemistry. The study adopted the use of repeated measures design whereby 50 pre-service teachers were purposively used in the study. A 40-item Practical Chemistry Achievement Test (PCAT) which was subjected to expert validation and reliability test was used to obtain data for the study. A normality test was conducted using Kolmogorov-Smirnov test and it was revealed that the data were normally distributed (P > 0.005). The students were given two pre-test and post-measures before and after the 8-week treatment period. The data were analyzed using mixed design repeated measures analysis of variance and we found that students’ performance improved periodically with each testing period (F (3,147) = 109.475, P = 0.000 with an effect size of (ηp 2 ) = 0.916) after the treatment. The finding also revealed no significant differences in the performance of the students on the basis of gender. Our finding has some implications for lecturers, researchers and policy experts on the need to incorporate mobile learning in education. Our finding provides insights on the effectiveness of mobile learning towards enhancing students’ chemistry practical knowledge. Mobile learning Chemistry Practical Educational Technology Pre-service teachers ADDIE Model ICT in Education Chemistry Education Figures Figure 1 Introduction Problem statement and study background Chemistry practicals are an essential part of science education that offers students a hands-on approach to learning and understanding complex chemical concepts and processes. This practical component is regarded as a core determinant of mastery in chemistry (Muleta & Seid, 2016 ; Köller et al., 2015 ). When students learn through practical activities, especially in chemistry, they tend to understand concepts better (Afyusisye & Gakuba, 2022 ; Shana & Abulibdeh, 2020 ). However, in Nigeria, the inadequacy of infrastructure and equipment reduces the effectiveness of chemistry practical sessions. Without proper laboratory facilities and essential equipment, students cannot engage in hands-on experiments thereby limiting their understanding of chemical principles and their ability to apply them in real-world contexts (Tsobaza & Njoku, 2021 ). Additionally, documented evidence (e.g., Chala, 2019 ; Tsobaza & Njoku, 2021 ) revealed that practical chemistry in Nigeria faces several difficulties affecting the effectiveness and quality of chemistry education. Ideally, practical sessions in chemistry should provide students with opportunities to engage in experiments that reinforce theoretical knowledge. Effective chemistry practicals require well-equipped laboratories, access to various chemicals and reagents, and sufficient safety measures to ensure a conducive learning environment. Nonetheless, over the years, the absence of laboratory equipment and infrastructure has limited the scope of experiments and practical demonstrations thereby affecting students’ performance in chemistry (Muoneke et al., 2021 ). In the Nigerian context, many studies (e.g., Ogunode & Olatunde-Aiyedun, 2020; Idah & Eya, 2018 ; Monday & Mallo, 2021 ; Udogu & Emendu, 2017 ) have noted that the lack of sufficient laboratory facilities and infrastructure significantly hinders the practical aspect of teacher training. This leads to deficiencies in essential practical skills which adversely affect students’ academic performance. Moreover, while existing studies conducted outside Nigeria (e.g., Kouhi & Rahmani, 2022 ; Demir & Akpinar, 2018 ; Ali et al., 2024; Han et al., 2022 ; Amasha et al., 2021 ; Hsu et al., 2023 ; Alkhateeb & Al-Duwairi, 2019 ; Vazquez-Cano et al., 2022; Essel et al., 2022 ) revealed the effectiveness of mobile learning on students' achievement, very few studies (e.g., Oyelere, 2017; Mohammed et al., 2024a ; Mohammed et al., 2024b ; Falode et al., 2022 ) have been conducted in Nigeria to enhance students’ performance and these studies were not focused on chemistry practical. In order to address this problem, we developed and evaluated a mobile application to enhance pre-service teachers’ performance in practical chemistry among colleges of education in Nigeria. Our study aims to provide insights into the potential of mobile learning to enhance pre-service teachers’ performance in practical chemistry in Nigeria. Çalışır et al. (2022) stated that mobile learning is a type of distance education in which students use mobile devices for learning considering their convenience and comfort. One of the main benefits of mobile learning is that it allows learners to access materials on the go, which is particularly useful for busy learners who may not have time to sit down on a computer (Ali et al., 2024). The use of technology in modern classroom environments has become a trend due to the constant emergence of studies (e.g., Falode & Mohammed, 2023a ; Falode & Mohammed, 2023b ; Choi & Chung, 2021 ) seeking to incorporate various technologies in teaching and learning. Learning with the help of technology fosters creativity and empowers learners to be autonomous (Ali et al., 2024). According to Talan ( 2020 ), mobile devices are now a major element in the instruction processes. The advent of mobile devices has changed our ways of learning, and with high-speed processors and internet, mobile devices have become the desired choice in education (Yu et al., 2022 ). Mobile learning is vital for teaching and learning as it facilitates learning through features such as easy usability (Kukulska-Hulme & Viberg, 2018 ; Tatte et al., 2022 ), portability (Hameed et al., 2022 ), ubiquity (Wang, 2022 ), ensuring ease of learning (Stecuła & Wolniak, 2022 ), cost-effectiveness (Inel-Ekici & Ekici, 2022 ), improving communication and social interaction (Park et al., 2012), providing continuous personalized learning (Dalili Saleh et al., 2022 ), exclusion of spatial and temporal limitations (Belousova et al., 2022 ), facilitating collaborative learning (Kumar, 2022), fostering autonomous learning (Elaish et al., 2023 ), seamless connectivity (Hussein et al., 2022 ), mobility (Rohanai et al., 2022 ), and blended learning (Buraimoh et al., 2023 ). M-learning has been utilized to provide access to educational resources and interactive learning materials through mobile applications or platforms like digital textbooks, educational videos, simulations, and interactive quizzes. This allows students in remote areas or with limited access to traditional educational resources to learn at their own pace and convenience. The use of mobile devices for educational purposes can support and improve the learning process anytime, anywhere, and mobile learning is an emerging aspect of educational technology at different levels of education (Nikolopoulou, 2020 ). Mobile learning application enables learning anytime and anywhere. It is instantly accessible, helping to fuel curiosity, collaborate with others, and enrich experiences (Naciri et al., 2020 ; Zhang, 2022). Mobile learning applications have the potential to overcome challenges faced by the traditional education system in Nigeria, such as insufficient educational resources, inadequate infrastructure, and limited access to qualified teachers (Tabowei, 2021 ). These applications provide students with access to a wide range of educational materials, including video lectures, podcasts, e-books, and interactive learning tools, regardless of their location. Furthermore, mobile learning facilitates self-regulated learning, creates collaborative learning environments (Mehra, 2022), and helps students improve their technical skills (Kumar, 2022). Empirical review and hypothesis development Mobile learning and students’ achievement There are many studies conducted in the field of mobile learning as they enhance students’ achievement. For example, Kouhi and Rahmani ( 2022 ) designed and developed a mobile application for teaching triple multiplication to young pre-school students and the finding revealed that students’ achievement improved significantly after exposure to mobile learning app. Mohammed et al. ( 2024a ) developed an educational game to teach educational technology postgraduate students during the COVID-19 pandemic and findings revealed that students’ performance significantly increased after exposure to mobile educational game to a very large extent. Demir and Akpinar ( 2018 ) studied the effect of mobile learning technology on undergraduate students’ achievement in graphic education and the findings revealed that the use of mobile application significantly improved the achievement of students. Han et al. ( 2022 ) designed and tested the effectiveness of an artificial intelligence mobile chatbot on college of nursing students’ performance and the findings revealed that the use of artificial intelligence mobile chatbot improved the performance of students. Amasha et al. ( 2021 ) developed a java-based mobile application to teach primary school mathematics students and findings revealed that mobile application enhanced students’ performance. Hsu et al. ( 2023 ) designed and tested the effectiveness of an expert-decision-making mobile application to teach college students climate concepts and their findings revealed that students’ performance significantly improved after exposure to the mobile application. Oyelere et al. (2017) designed, developed and evaluated a mobile application for teaching of computing education and the findings revealed the adoption of mobile application improved students’ performance. Vazquez-Cano et al. (2022) designed and tested the effectiveness of mobile chatbot towards improving undergraduate students’ punctuation skills and their findings revealed that students punctuation skills significantly improved after exposure to artificial intelligence mobile learning. Ali et al. (2024) developed a mobile application for teaching undergraduate English language and the findings revealed that the performance of students improved significantly after exposure to mobile application. Essel et al. ( 2022 ) designed and tested the effectiveness of a mobile virtual assistant on undergraduate students’ programming skills and their findings revealed students’ performance improved significantly. Mohammed et al. ( 2024b ) tested the effectiveness of students’ performance in a microteaching course using Telegram and WhatsApp mobile applications and the findings revealed that students’ achievement improved significantly after exposure to instruction using mobile learning. Falode et al. ( 2022 ) designed and evaluated a mobile application on educational technology students’ achievement and the findings revealed that the adoption of mobile application in teaching and learning of educational technology significantly improved students’ performance. Alkhateeb and Al-Duwairi ( 2019 ) designed and tested the effectiveness of mobile application to teach geometry concepts to undergraduate students and their study revealed that students’ performance increased significantly after exposure to mobile learning. The foregoing discuss revealed the usefulness of mobile learning towards enhancing students’ performance in various disciplines. The studies we reviewed focused on educational technology (Mohammed et al., 2024a ; Falode et al., 2022 ; Demir & Akpinar, 2018 ), micro-teaching (Mohammed et al., 2024b ), programming and computing (Oyelere et al., 2017; Essel et al., 2022 ); English and punctuation skills (Vazquez-Cano et al., 2022; Ali et al., 2024), nursing (Han et al., 2022 ), mathematics (Amasha et al., 2021 ; Kouhani & Rahmani, 2023; Alkhateeb & Al-Duwairi, 2019 ) and climate change concepts (Hsu et al., 2023 ). While the preceding studies revealed a unanimous finding in terms of the effectiveness of mobile learning towards enhancing students’ performance in different fields, however, the studies reviewed were not conducted on Chemistry practical which therefore leaves a gap that needs to be filled. As a result, there is need for more studies to be conducted to close these gaps. In view of the preceding discussion, we formed the following hypothesis: Ho 1 There is no significant difference in the mean achievement scores of pre-service teachers taught practical chemistry before and after exposure to the mobile learning application Mobile learning and gender differences in students’ achievement Gender as a moderating variable continues to play a role in terms of students’ performance in mobile learning technology. For example, Hilao and Wichadee ( 2017 ) checked whether there is any significant difference in male and female students’ performance after exposure to mobile learning and the findings revealed that the students did not differ in their performance based on gender. The study of Falode et al. ( 2022 ) revealed that there was a significant difference in the achievement of male and female educational technology students exposed to mobile learning in favour of female students. The study of Sandu and Gide (2019) did not find any significant difference in terms of gender after exposure to mobile learning using a chatbot. The study of Daud et al. ( 2021 ) revealed a significant difference in the achievement of male and female students exposed to mobile learning. Additionally, the study of Essel et al. ( 2022 ) found no significant difference in the achievement of male and female students after using mobile chatbot for instruction. Mohammed et al. ( 2023 ) checked gender difference in students’ microteaching performance in a mobile learning using Telegram and WhatsApp and the findings revealed there was no significant difference in the achievement of male and female students. In line with the above studies reviewed, there seems to be no unanimous agreement regarding the role of gender in terms of students’ performance during a mobile learning strategy. From the studies reviewed above, there are contradictory findings. For example, while some studies (e.g., Falode et al., 2022 , Daud et al., 2021 ) found a significant difference in students’ performance based on gender, some of the studies (e.g., Hilao & Wichadee, 2017 ; Sandu & Gide, 2019; Essel et al., 2022 ) found no significant difference in terms of students’ performance in a mobile learning. Therefore, this creates another gap that needs to be filled in order to add more literature in terms of gender differences in mobile learning. Thus, in view of this development above, we formed the next hypothesis: Ho 2 There is no significant difference in the mean achievement scores of male and female pre-service teachers taught practical chemistry before and after exposure to the mobile learning application Methodology The study adopted a repeated measures design to evaluate the effectiveness of the mobile learning application whereby one group of pre-service students where studied over a period of 8 weeks in order to ascertain the periodic improvement in their achievement as a direct influence of the treatment. Edmonds and Kennedy ( 2017 ) observed that repeated measures design permits researchers to acquire different data points over a long period with the main purpose of checking the observed variation in performance as a result of treatment or time. In this type of design, the various test conditions serve as a control for the different treatment settings. As a result, in this design, we subjected the participants to different testing conditions before and after the treatment over a long period of time to measure their periodic improvement in performance as a result of the mobile learning treatment issued to them. This design has been used in various studies (e.g., Mohammed et al., 2024a ; Mohammed & Bello 2024 ). Participants Purposive sampling technique was used to select 50 year-two pre-service teachers from Federal College of Education (FCE) Kontagora, Niger State Nigeria for the 2023/2024 academic session. The choice of year two pre-service teachers was employed because the course content covered was domiciled in year two chemistry course contents. As at the time of conducting this study, there were a total of 757 pre-service teachers. Instrument The research instrument used in this study was the Practical Chemistry Achievement Test (PCAT) which consisted of 40-item multiple-choice questions drawn using Benjamin Bloom’s table of specification. We used Bloom’s table of specification in order to spread the entire questions across the various sub-levels of the cognitive domain of education as specified by Bloom. The test items thus covered the areas of knowledge, comprehension, application, analysis, synthesis and evaluation in order to thoroughly measure the students’ cognitive understanding at the end of the treatment using mobile application. The instrument had two sections: section A and section B. Section A deals with the students’ demographic data while section B consists of the 40-item multiple choice questions with options A-D, containing one correct answer and three distracters. In order to ascertain the item difficulty index of the test, we conducted an item analysis where a P-value of 0.36 was obtained, thus falling in accordance with the acceptable average difficulty range criteria of 0.30 to 0.49 (Hasançebi et al., 2020 ). As scoring criteria, each correct answer was scored 2 marks while an incorrect answer was scored 0. This therefore means that the maximum score obtainable from the test was 40, while the minimum score was 0. Validity and Reliability The instrument validation was done in two phases. First, we subjected the PCAT to expert validation in order to determine its validity by giving it to experts in the field of chemistry education and measurement and evaluation. These experts checked the face and content validity of the instrument and provided useful insights in the final draft of the instrument. Their corrections in terms of lengths of questions, eliminating easy options, chronological arrangement and language clarity were then implemented in the final production of the instrument used in the study. As for the mobile learning application, we first created a prototype that was given to experts in the field of instructional design, educational technology, computer science and software engineering in order the check the user interphase, selection pane, colour contrast and user friendliness during operation. Their observations were then corrected and the final mobile application was finally produced. In order to ascertain the reliability of the PCAT, copies of the instrument were given to different set of students that were within the population but outside the sample in a single administration and a reliability figure of 0.79 was obtained using Kuder-Richardson 20 formula. During the pilot test, some of the test items returned with a very weak value and were therefore removed in the final copy of the instrument to be used for the study. Development of mobile learning application The mobile application used in this study was designed using the ADDIE instructional design model as stated below: Analysis The analysis phase in developing the chemistry practical mobile learning application involves a comprehensive examination of need assessment and student profiling. The need assessment involves engaging with educators and students to identify challenges in chemistry practicals. This includes evaluating existing materials such as textbooks, lab manuals, and online resources to streamline the course development process. By focusing on learners' needs and the learning context, the analysis phase ensures the effective utilization of available resources and sets the foundation for the subsequent stages of course design and implementation. Design During the design phase of the chemistry practical mobile learning application, we created a seamless and effective user experience through careful user interface (UI) design, a well-structured content layout, and the development of a functional prototype. A computer programmer and one of the researchers were involved in designing the package. The UI design includes selecting appropriate colors, typography, and icons that align with the subject while maintaining a consistent and visually appealing look. Ease of navigation is prioritized by ensuring users can easily access sections such as theory, experiments, quizzes, and supplementary resources. Choices regarding the programming language, operating system (Android), and memory size were made. Multimedia elements like videos and images are strategically embedded to enhance comprehension. Practice exercises and quizzes are integrated into relevant lessons to promote active learning. Development In the development phase, the chemistry practical course content involves creating comprehensive instructional materials, organizing lesson sequences, and constructing relevant assessments to enhance learning outcomes. This phase considers factors such as the progression of difficulty, logical flow of concepts, and resource efficiency. Both formative assessments for ongoing feedback and summative assessments for final evaluation were formulated. The mobile application was developed using Kodular, a no-code platform for Android applications. Learning content, assessments, and YouTube video links were managed via an e-learning platform built with JavaScript, HTML, CSS, and PHP. User authentication protocols were integrated to validate identities during login and interactions. Authentication and authorization mechanisms were implemented to protect content and manage access securely. User registration and login functionalities were designed with advanced security measures. Self-assessment exercises were incorporated in each unit to gauge cognitive levels. Data integration was employed to ensure efficient communication between the app's front-end and back-end systems, with a robust database architecture established to store user profiles and track progress. An API was developed to facilitate seamless data exchange, maintaining consistency of user information and progress across devices. Implementation We trained research assistants that helped the various students to install the mobile learning application. Additionally, an orientation session was conducted to familiarize students with the application's usage. Evaluation The evaluation phase of a chemistry practical course includes a post-test administered after the course, designed to assess their comprehension of practical chemistry topics taught with the application. Experimental procedure Even though the study involved the use of human subject, ethical clearance was waived as per the Nigerian Ministry of Health’s code of research ethics (2007). We also obtained written informed consent in order to use the sampled students for the study where we also assured the participants of the confidentiality of their data for research purpose only. The students signed the consent form permitting the conduct of this study. The researchers issued two separate pre-test measures (pre-test 1 and pre-test 2) within two weeks interval prior to the commencement of the study in order to measure the students’ entry behavior. Thereafter, the students were subjected to the treatment using the mobile learning application over a period of 8 weeks. The students interacted with the mobile application to learn the various chemistry practical concepts. For every week, the teacher introduced the concepts to be studied by the students where they learnt at their own pace once every week. The students planned their learning process in an individualized learning with the teacher serving as facilitator. The contents covered were balancing of redox reaction, redox titrations, and measurement of pH using indicators. After the 8-week treatment period, the researchers then administered the posttest measures (posttest 1 and posttest 2) within a period of two weeks. All other sources of information was blocked during the treatment to make sure the students concentrate and that data came from the mobile application only. In each of the test period, the students were required to provide answers within one hour. Method of analysis The various results we obtained during the experiment were subjected to analysis using a mixed design repeated measures analysis of variance (RM-ANOVA) in order to compute the within subject effect of the various test conditions. We also computed the descriptive statistics using mean and standard deviation. Additionally, we conducted a normality test using Kolmogorov-Smirnov test and the result revealed that the data was normally distributed (P > 0.05). We also computed the Skewness and Kurtosis of the data set and the values obtained falls within the acceptable range of ± 2 and ± 7 for Skewness and Kurtosis respectively (Hair et al., 2010 ; George & Mallery, 2010 ; Bryne, 2010 ) which therefore warrants the use of parametric statistics. In order to ensure that the assumption of sphericity was not violated as one of the basic assumptions of RM-ANOVA, we computed the Mauchly’s test of sphericity and the value revealed that P > 0.05 which means that the variance of all the within subject pairings of the different test conditions is equal. Sidak post-hoc test was also conducted to show the direction of the significant differences. The result was computed using SPSS software version 25. Results Table 1 Mean and standard deviation of students’ achievement scores at different test occasions Test N Mean SD Pretest 1 50 47.02 10.23 Pretest 2 50 48.34 10.76 Posttest 1 50 66.63 9.01 Posttest 2 50 74.68 9.19 Table 1 revealed the mean and standard deviation of students’ achievement scores at different test period. The table revealed the students had M = 47.02, SD = 10.23, and M = 48.34, SD = 10.76 at pre-test 1 and pre-test 2 respectively. The table also revealed that the student obtained M = 66.63, SD = 9.01, and M = 74.68, SD = 9.19 at posttest1 and posttest 2 respectively. This reveals that the students’ performance periodically improved in each case as a result of the treatment effect. Table 2 Kolmogorov-Smirnov test of normality Time Statistic Df P Pretest 1 .630 50 .241 * Pretest 2 .476 50 .344 * Posttest 1 .742 50 .264 * Posttest 2 .542 50 .572 * * Not Significant, P > 0.05 Table 2 revealed the Kolmogorov-Smirnov normality test conducted to determine whether the data was normally distributed. The table revealed that P > 0.05 for all the different four test occasions. Therefore, the hypothetical assumption that the data set came from a normal distribution to warrant the use of parametric inferential statistics is hereby confirmed. Table 3 Mean and standard deviation of male and female students’ achievement at different test occasions Test N Gender Mean SD Pretest 1 31 Male 48.32 10.47 19 Female 44.89 9.71 50 Total 47.02 10.23 Pretest 2 31 Male 47.06 10.29 19 Female 50.42 11.45 50 Total 48.34 10.76 Posttest 1 31 Male 64.61 9.29 19 Female 66.73 8.79 50 Total 66.63 9.01 Posttest 2 31 Male 72.67 9.50 19 Female 77.94 7.22 50 Total 74.68 9.19 Table 3 revealed the performance of male and female students at different test occasions after exposure to mobile learning application. The students had M = 48.32, SD = 10.47 and M = 44.89, SD = 9.71 at pre-test 1 for male and female respectively. It also revealed that M = 47.06, SD = 10.29 and M = 50.42 and SD = 11.45 at pre-test 2 for male and female respectively. Furthermore, the table also revealed that the students had M = 64.61, SD = 9.29 and M = 66.63, SD = 9.01 at posttest 1 for male and female respectively. It also revealed that M = 72.67, SD = 9.50 and M = 77.94 and SD = 7.22 at posttest 2 for male and female respectively. Testing of hypothesis Ho 1 There is no significant difference in the mean achievement scores of pre-service teachers taught practical chemistry before and after exposure to the mobile learning application Table 4 RM-ANOVA of students’ achievement in the various within subject test occasions Source Type III Sum of Squares df Mean Square F P ηp 2 Time Sphericity Assumed 28078.575 3 9359.525 109.475 .000 * .916 Greenhouse-Geisser 28078.575 2.872 9775.148 109.475 .000 .916 Huynh-Feldt 28078.575 3.000 9359.525 109.475 .000 .916 Lower-bound 28078.575 1.000 28078.575 109.475 .000 .916 Error(Time) Sphericity Assumed 12567.675 147 85.494 Greenhouse-Geisser 12567.675 140.750 89.291 Huynh-Feldt 12567.675 147.000 85.494 Lower-bound 12567.675 49.000 256.483 * Significant P < 0.05 Table 4 reveals the RM-ANOVA of students’ performance at different test occasions. The table revealed that F (3,147) = 109.475, P = 0.000 and Partial Eta Squared effect size (ηp 2 ) = 0.916. This shows that the null hypothesis is hereby rejected, which indicates that there was a significant difference in the students’ performance at different test occasions. Thus, the use of mobile applications has a significant effect on students’ performance. Additionally, the effect size (ηp 2 ) of 0.916 means that 91.6% of the total variance in students’ performance is linked to the use of mobile learning application. What this means is that the use of mobile learning application improved students’ performance by 91.6% during the treatment period. Furthermore, the table also revealed that Mauchly’s test of sphericity for the within subject pairings was equally not significant (Mauchly (W) = 0.938; P = 0.691). This therefore means that the variance of all the within subject effect in all the different four test occasions is equal, thus satisfying the assumption of sphericity. In order to check the direction of the differences in significance, Sidak post-hoc pairwise comparison was conducted in Table 5 . Table 5 Sidak post-hoc pairwise comparisons of students’ performance at four different test occasions (I) Time (J) Time Mean Difference (I-J) Std. Error P 95% Confidence Interval for Difference Lower Bound Upper Bound Pretest 1 Pretest 2 -1.320 2.020 .987 -6.858 4.218 Posttest 1 -19.640 * 1.735 .000 -24.395 -14.885 Posttest 2 -27.660 * 1.770 .000 -32.512 -22.808 Pretest 2 Pretest 1 1.320 2.020 .987 -4.218 6.858 Posttest 1 -18.320 * 1.994 .000 -23.785 -12.855 Posttest 2 -26.340 * 1.894 .000 -31.533 -21.147 Posttest 1 Pretest 1 19.640 * 1.735 .000 14.885 24.395 Pretest 2 18.320 * 1.994 .000 12.855 23.785 Posttest 2 -8.020 * 1.654 .000 -12.554 -3.486 Posttest 2 Pretest 1 27.660 * 1.770 .000 22.808 32.512 Pretest 2 26.340 * 1.894 .000 21.147 31.533 Posttest 1 8.020 * 1.654 .000 3.486 12.554 * Significant P < 0.05 Table 5 revealed the post-hoc pairwise comparison of students’ performance in four different test occasions after exposure to mobile learning application. The table revealed that even though there was a significant difference between the pre-test and posttest scores of the students which indicates a periodic increase in performance as a result of the treatment, the significant difference between posttest 2 and pretest 1 was higher, followed by the significant difference between posttest 2 and pre-test 2. Ho 2 There is no significant difference in the mean achievement scores of male and female pre-service teachers taught practical chemistry before and after exposure to the mobile learning application Table 6 RM-ANOVA of male and female students’ performance at four different test occasions Source Type III Sum of Squares df Mean Square F P ηp 2 Treatment * Gender Sphericity Assumed 515.042 3 171.681 2.051 .109 * .021 Greenhouse-Geisser 515.042 2.852 180.579 2.051 .113 .021 Huynh-Feldt 515.042 3.000 171.681 2.051 .109 .021 Lower-bound 515.042 1.000 515.042 2.051 .159 .021 Error(Treatment) Sphericity Assumed 12052.633 144 83.699 Greenhouse-Geisser 12052.633 136.904 88.037 Huynh-Feldt 12052.633 144.000 83.699 Lower-bound 12052.633 48.000 251.097 * Not Significant P > 0.05 Table 6 shows the RM-ANOVA of male and female students’ performance at different test occasions after exposure to mobile learning application. The table revealed that F (3,144) = 2.051, P = 0.109, with an effect size of 0.21. This means that the hypothesis is hereby retained, thus signifying no significant difference in the mean achievement of male and female students after exposure to mobile learning. The partial eta squared effect size (ηp 2 ) of 0.21 means that 21.0% of the total variance in the performance of male and female students after the treatment is linked to the mobile learning treatment. Discussion The main aim of the study was to determine the effectiveness of a designed mobile application on pre-service teachers’ performance in practical chemistry. After exposing the students to instruction using the mobile learning application, we discovered that the students’ performance increased periodically in each of the test period as a result of the treatment. The null hypothesis was rejected which therefore means that there was indeed a significant difference in the performance of students exposed to mobile learning application. The finding of the study revealed that mobile learning application is directly linked to 91.6% of the total variance in students’ performance after the treatment given the effect size of 0.916 that was recorded. This means that mobile learning application was responsible for improving students’ performance by 91.6% which is categorized as a very high effect (Cohen, 1988 ). Our finding could be that with a mobile application, students have the liberty to learn at their own pace and construct meaning out of what they are being taught. This improvement in performance is very possible given the various features of mobile learning which makes it suitable for learning like portability (Hameed et al., 2022 ), ubiquity (Wang, 2022 ), easing of learning process (Stecuła & Wolniak, 2022 ), improvement in communication and social interaction (Park et al., 2012), fostering personalized learning (Dalili Saleh et al., 2022 ), facilitating collaborative learning (Kumar Basak et al., 2018), ensuring autonomous learning (Elaish et al., 2023 ), mobility (Rohanai et al., 2022 ), and facilitating blended learning opportunities (Buraimoh et al., 2023 ). Additionally, mobile learning provides students with different learning resources to learn from like video lectures and interactive learning contents which could appeal to many learning preferences of students as noted in this finding. Additionally, mobile learning provides an avenue for learning to take place anywhere, anytime which could help to increase student’ curiosity towards learning. The finding agrees with the several empirical studies (e.g., Kouhi & Rahmani, 2022 ; Mohammed et al., 2024; Demir & Akpinar, 2018 ; Amasha et al., 2021 ; Han et al., 2022 ; Oyelere et al., 2017; Hsu et al., 2023 ; Ali et al., 2024; Vazquez-Cano et al., 2022; Essel et al., 2022 ; Mohammed et al., 2024b ; Falode et al., 2022 ) that we reviewed in this study. Our finding, therefore, provides an insight into the effectiveness of enhancing pre-service teachers’ performance in practical chemistry amongst pre-service teachers from the Nigerian perspectives. Furthermore, the finding of this study revealed that there was no significant difference in the performance of male and female students after exposure to mobile learning. This means that both male and female students performed almost equally since the difference was not significant even though male students achieved a higher mean. The result also revealed that 21.0% of the total variance in male and female students’ performance is linked to mobile learning given the effect size of 0.21 that was noticed which, according to Cohen ( 1988 ), is categorized as small. This finding, however, could be due to individual differences which now form the basis for further studies to be conducted to further add to the frontiers of knowledge in the field of gender differences in mobile learning. This finding agrees with a number of studies (e.g., Hilao & Wichadee, 2017 ; Sandu & Gide, 2019; Essel et al., 2022 ; Mohammed et al., 2023 ) we reviewed in this study. However, on the contrary, it disagrees with some studies (e.g., Falode et al., 2022 ; Daud et al., 2021 ) we reviewed which revealed a significant difference based on gender. These inconsistent findings, however, could be due to individual differences. This study thus now forms the basis for further studies to be conducted in order to further add to the frontiers of knowledge in the field of gender differences in mobile learning. Implications of findings for practice and research The finding of this study has a number of implications for chemistry teachers, policy makers and other science education researchers in Nigeria. It underscores the fact that if mobile learning could be incorporated in teaching chemistry practical in Nigeria, students’ performance could be enhanced for it has what it takes to eliminate poor infrastructure and inadequate equipment. This finding revealed that the use of mobile learning has what it takes to encourage personalized learning thereby enabling students to construct meaning out of the learning process at their own pace thus leading to increase in performance. From the research perspectives, this finding has equally added to the scarce literature in the areas of research and development of mobile application for teaching chemistry practical in Nigeria. Conclusion This study revealed that the use of mobile learning for teaching practical chemistry amongst pre-service teachers is very effective and it equally enhances students’ performance thereby leading to the tackling of poor performance and eliminating challenges during practical. Therefore, lecturers should be taught on how to implement mobile learning in their teaching and learning. Additionally, workshops should be organized in order to teach lecturers how to incorporate mobile learning. Limitation and further research directions This study has a number of limitations that should be addressed. First of all, the study was conducted on pre-service teachers of colleges of education in a Nigerian college of education; thus, similar study should be conducted on other pre-service teachers especially at the university levels to further test the effectiveness of the mobile learning application. The study was limited to a few practical chemistry aspects and as such, generalizations could not be drawn; therefore, similar studies should be conducted in other areas of chemistry. The study sample was small and therefore the generalizability of the study could not be established; as a result, similar studies should be conducted using a larger population in order to further establish the effectiveness of the mobile learning application. Declarations Author Contribution FMS: Conceptualisation, writing draft, editing and approval. IAM: Conceptualisation, writing draft, editing, methodology, editing, methodology, data analysis and approval. BMS: Conceptualisation, data collection, writing draft, development of mobile application and approval. FA: Project administration, supervision and approval AS: Technical support, software and approval. Acknowledgement The authors appreciate the students that participated in the study. Data Availability Data is available from the corresponding author upon reasonable request. References Afyusisye, A., & Gakuba, E. (2022). The effect of the chemistry practicals on the academic performance of Ward Secondary School students in Momba District in Tanzania. Journal of Mathematics and Science Teacher, 2 ( 2 ), em019. https://doi.org/10.29333/mathsciteacher/12397 Alkhateeb, M. A., & Al-Duwairi, A. M. (2019). The Effect of Using Mobile Applications (GeoGebra and Sketchpad) on the Students' Achievement. International Electronic Journal of Mathematics Education, 14 (3), 523–533. Amasha, M. A., Areed, M. F., Khairy, D., Atawy, S. M., Alkhalaf, S., & Abougalala, R. A. (2021). 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Mobile inquiry and inquiry-based science learning in higher education: Advantages, challenges, and attitudes. Asia Pacific Education Review, 23 (3), 427–444. Köller, H. G., Olufsen, M., Stojanovska, M., & Petrusevski, V. (2015). Practical work in chemistry and its goals and effects. Chemistry in Action, 106, 37–50. Kouhi, M., & Rahmani, M. (2022). Design and Development of a Mobile Application for Teaching Triple Multiplication to Preschool Children. SN Computer Science, 3:156. https://doi.org/10.1007/s42979-022-01033-z Kukulska-Hulme, A., & Viberg, O. (2018). Mobile collaborative language learning: State of the art. British Journal of Educational Technology, 49 (2), 207–218. Kumar, J.A., Osman, S., Sanmugam, M., & Rasappan, R. (2022). Mobile Learning Acceptance Post Pandemic: A Behavioural Shift among Engineering Undergraduates. Sustainability, 14 (6), 3197. https://doi.org/10.3390/su/14063197 Mehra, A., Rajput, S. & Paul, J. (2022). Determinants of adoption of latest version smartphones: Theory and evidence. Technological Forecasting and Social Change, 175 (121410). https://doi.org/10.1016/j.techfore.2021.121410 . Mohammed, I. A., & Bello, A. (2024). Performance of mathematics students using video learning in flipped and flipped collaborative learning settings. Pedagogical Research, 9 (3), em0213. https://doi.org/10.29333/pr/14699 Mohammed, I. A., Falode, O. C., Kuta, I. I., & Bello, A. (2024a). Effect of game-based learning on educational technology Students’ performance: A case of simple repeated measures approach. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12593-3 Mohammed, I. A., Kuta, I. I., Falode, O. C., & Bello, A. (2024b). Comparative performance of undergraduate students in micro teaching using Telegram and WhatsApp in collaborative learning settings. Journal of Mathematics and Science Teacher, 4 (2), em063. https://doi.org/10.29333/mathsciteacher/1441 Mohammed, I. A., Kuta, I. I., & Bello, A. (2023). Gender difference in undergraduates’ micro-teaching performance using Telegram and WhatsApp platforms in collaborative learning settings. Mediterranean Journal of Social & Behavioral Research, 7 (1), 1–8. https://doi.org/10.30935/mjosbr/13665 Monday, O. K., & Mallo, G. D. (2021). Higher Education in Nigeria: Challenges and Suggestions. Middle European Scientific Bulletin, 16 , 55–61. Muleta, T., & Seid, M. (2016). Factors affecting implementation of practical activities in science education in some selected secondary and preparatory schools of Afar region. African Journal of Chemical Education, 6 (2), 123–142. Muoneke, N. M., Asagha, E. N., & Muoneke, U. C. (2021) Chemistry education and national commission for colleges of education minimum standards: an analysis of trends, challenges and development of cloud-based applications. Multidisciplinary Journal of Academic Excellence, 21(1), 2141–3215. Naciri, A., Baba, M. A., Achbani, A., & Kharbach, A. (2020). Mobile learning in higher education: unavoidable alternative during COVID-19. Aquademia, 4(1), ep20016. https://doi.org/10.29333/aquademia/822 Nikolopoulou, K. (2020). Secondary education teachers’ perceptions of mobile phone and tablet use in classrooms: benefits, constraints and concerns. Journal of Computers in Education, 7 (2), 257–275. https://doi.org/10.1007/s40692-020-00156-7 Olatunde-Aiyedun, T.G., & Ogunode, N.J. (2021). School Administration and effective teaching methods in Science Education in Nigeria. International Journal on Integrated Education, 4 (2), 145–161. https://doi.org/10.13140/RG.2.2.11502.54080 Oyelere, S. S., Suhonen, J., Wajiga, G. M., & Sutinen, E. (2018). Design, development, and evaluation of a mobile learning application for computing education. Education and Information Technologies, 23, 467–495. Rohanai, R., Ahmad, M. F., Rameli, M. R. M., Azrul, W., Hassan, S. W., & Abd Mutalib, N. N. (2022). The attitudes of MTUN students towards m-learning usage during COVID-19 pandemic. International Journal of Information and Education Technology, 12 (5), 406–413. Shana, Z., & Abulibdeh, E. S. (2020). Science practical work and its impact on high students’ academic achievement. Journal of Technology and Science Education, 10 (2), 199–215. https://doi.org/10.3926/jotse.888 . Stecuła, K., & Wolniak, R. (2022). Advantages and disadvantages of e-learning innovations during COVID-19 pandemic in higher education in Poland. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 159. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48 (6), 1273–1296. Tabowei, A. (2021). Technology enhance learning: A case study of the potentials of mobile technologies in Nigerian College of Education (Doctoral dissertation). Talan, T. (2020). The effect of mobile learning on learning performance: A meta-analysis study. Educational Sciences: Theory and Practice, 20(1), 79–103. Tatte, E., Ramachandran, M., & Saravanan, V. (2022). Mobile learning: A new methodology in education system. Contemporaneity of Language and Literature in the Robotized Millennium, 4(1), 1–9. Tsobaza, M. K., & Njoku, Z. C. (2021). Effect of practical chemistry teaching strategies on students' acquisition of practical skills in secondary schools in Kogi State. African Journal of Science, Technology and Mathematics Education, 6 (1), 195–203. Udogu, M. E., & Emendu, N. B. (2017). Impact of practical activities in chemistry laboratory exercise in senior secondary school in Nigeria. Journal of Environmental Sciences, 2 (3), 59–67. Vazquez-Cano, E., Mengual-Andr´es, S., & Lopez-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18, 33. https://doi.org/10.1186/s41239-021-00269-8 Wang, X. (2022). Mobile learning in Chinese higher education: student perspectives, advantages and challenges. In Proceedings of the 14th International Conference on Education Technology and Computers (pp. 207–211). Yu, Z., Yu, L., Xu, Q., Xu, W., & Wu, P. (2022). Effects of mobile learning technologies and social media tools on student engagement and learning outcomes of English learning. Technology, Pedagogy and Education, 31 (3), 381–398. Zhang, M., Chen, Y., Zhang, S., Zhang, W., Li, Y., & Yang, S. (2022). Understanding mobile learning continuance from an online-cum-offline learning perspective: A SEM-neural network method. International Journal of Mobile Communication, 20, 105–127. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5071560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369542963,"identity":"6ef963d4-d06b-4f3e-9dde-dfcb71935c60","order_by":0,"name":"Favour Mosunmola Sobowale","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Favour","middleName":"Mosunmola","lastName":"Sobowale","suffix":""},{"id":369542965,"identity":"c9a34072-9ae2-4204-b69e-be7d07e49f25","order_by":1,"name":"Ibrahim Abba Mohammed","email":"data:image/png;base64,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","orcid":"","institution":"Federal University Kashere","correspondingAuthor":true,"prefix":"","firstName":"Ibrahim","middleName":"Abba","lastName":"Mohammed","suffix":""},{"id":369542967,"identity":"82d12f1e-0d9d-478c-bc31-c440f668de43","order_by":2,"name":"Berechiah Manji Samson","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Berechiah","middleName":"Manji","lastName":"Samson","suffix":""},{"id":369542968,"identity":"b1caa6df-8934-42ea-885b-aa5413aaaf7e","order_by":3,"name":"Fati Ali","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Fati","middleName":"","lastName":"Ali","suffix":""},{"id":369542969,"identity":"47fc3aeb-ef55-4be4-b9dc-f02c04f5177d","order_by":4,"name":"Abdulazeez Sadiku","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Abdulazeez","middleName":"","lastName":"Sadiku","suffix":""}],"badges":[],"createdAt":"2024-09-11 13:36:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5071560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5071560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68270921,"identity":"22fcf3e6-c045-45b9-a334-64f9298b4376","added_by":"auto","created_at":"2024-11-05 13:40:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259326,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreenshot of the mobile application interphase\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5071560/v1/0c1caf66c02261fe1c0029e9.png"},{"id":68271207,"identity":"53dbf5e0-efd8-4dd1-be6f-951ebb359cee","added_by":"auto","created_at":"2024-11-05 13:48:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027641,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5071560/v1/041612d4-3e3b-4df1-a207-31a2bc43fdde.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and evaluation of mobile learning application for practical chemistry among pre-service teachers ","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eProblem statement and study background\u003c/h2\u003e \u003cp\u003eChemistry practicals are an essential part of science education that offers students a hands-on approach to learning and understanding complex chemical concepts and processes. This practical component is regarded as a core determinant of mastery in chemistry (Muleta \u0026amp; Seid, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026ouml;ller et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When students learn through practical activities, especially in chemistry, they tend to understand concepts better (Afyusisye \u0026amp; Gakuba, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shana \u0026amp; Abulibdeh, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, in Nigeria, the inadequacy of infrastructure and equipment reduces the effectiveness of chemistry practical sessions. Without proper laboratory facilities and essential equipment, students cannot engage in hands-on experiments thereby limiting their understanding of chemical principles and their ability to apply them in real-world contexts (Tsobaza \u0026amp; Njoku, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, documented evidence (e.g., Chala, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tsobaza \u0026amp; Njoku, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed that practical chemistry in Nigeria faces several difficulties affecting the effectiveness and quality of chemistry education. Ideally, practical sessions in chemistry should provide students with opportunities to engage in experiments that reinforce theoretical knowledge. Effective chemistry practicals require well-equipped laboratories, access to various chemicals and reagents, and sufficient safety measures to ensure a conducive learning environment. Nonetheless, over the years, the absence of laboratory equipment and infrastructure has limited the scope of experiments and practical demonstrations thereby affecting students\u0026rsquo; performance in chemistry (Muoneke et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Nigerian context, many studies (e.g., Ogunode \u0026amp; Olatunde-Aiyedun, 2020; Idah \u0026amp; Eya, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Monday \u0026amp; Mallo, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Udogu \u0026amp; Emendu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) have noted that the lack of sufficient laboratory facilities and infrastructure significantly hinders the practical aspect of teacher training. This leads to deficiencies in essential practical skills which adversely affect students\u0026rsquo; academic performance. Moreover, while existing studies conducted outside Nigeria (e.g., Kouhi \u0026amp; Rahmani, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demir \u0026amp; Akpinar, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ali et al., 2024; Han et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Amasha et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hsu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Alkhateeb \u0026amp; Al-Duwairi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vazquez-Cano et al., 2022; Essel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed the effectiveness of mobile learning on students' achievement, very few studies (e.g., Oyelere, 2017; Mohammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Falode et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have been conducted in Nigeria to enhance students\u0026rsquo; performance and these studies were not focused on chemistry practical. In order to address this problem, we developed and evaluated a mobile application to enhance pre-service teachers\u0026rsquo; performance in practical chemistry among colleges of education in Nigeria. Our study aims to provide insights into the potential of mobile learning to enhance pre-service teachers\u0026rsquo; performance in practical chemistry in Nigeria.\u003c/p\u003e \u003cp\u003e\u0026Ccedil;alışır et al. (2022) stated that mobile learning is a type of distance education in which students use mobile devices for learning considering their convenience and comfort. One of the main benefits of mobile learning is that it allows learners to access materials on the go, which is particularly useful for busy learners who may not have time to sit down on a computer (Ali et al., 2024). The use of technology in modern classroom environments has become a trend due to the constant emergence of studies (e.g., Falode \u0026amp; Mohammed, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Falode \u0026amp; Mohammed, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Choi \u0026amp; Chung, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) seeking to incorporate various technologies in teaching and learning. Learning with the help of technology fosters creativity and empowers learners to be autonomous (Ali et al., 2024). According to Talan (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), mobile devices are now a major element in the instruction processes. The advent of mobile devices has changed our ways of learning, and with high-speed processors and internet, mobile devices have become the desired choice in education (Yu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mobile learning is vital for teaching and learning as it facilitates learning through features such as easy usability (Kukulska-Hulme \u0026amp; Viberg, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tatte et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), portability (Hameed et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ubiquity (Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ensuring ease of learning (Stecuła \u0026amp; Wolniak, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), cost-effectiveness (Inel-Ekici \u0026amp; Ekici, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), improving communication and social interaction (Park et al., 2012), providing continuous personalized learning (Dalili Saleh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), exclusion of spatial and temporal limitations (Belousova et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), facilitating collaborative learning (Kumar, 2022), fostering autonomous learning (Elaish et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), seamless connectivity (Hussein et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mobility (Rohanai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and blended learning (Buraimoh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eM-learning has been utilized to provide access to educational resources and interactive learning materials through mobile applications or platforms like digital textbooks, educational videos, simulations, and interactive quizzes. This allows students in remote areas or with limited access to traditional educational resources to learn at their own pace and convenience. The use of mobile devices for educational purposes can support and improve the learning process anytime, anywhere, and mobile learning is an emerging aspect of educational technology at different levels of education (Nikolopoulou, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mobile learning application enables learning anytime and anywhere. It is instantly accessible, helping to fuel curiosity, collaborate with others, and enrich experiences (Naciri et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang, 2022). Mobile learning applications have the potential to overcome challenges faced by the traditional education system in Nigeria, such as insufficient educational resources, inadequate infrastructure, and limited access to qualified teachers (Tabowei, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These applications provide students with access to a wide range of educational materials, including video lectures, podcasts, e-books, and interactive learning tools, regardless of their location. Furthermore, mobile learning facilitates self-regulated learning, creates collaborative learning environments (Mehra, 2022), and helps students improve their technical skills (Kumar, 2022).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEmpirical review and hypothesis development\u003c/h3\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMobile learning and students\u0026rsquo; achievement\u003c/h2\u003e \u003cp\u003eThere are many studies conducted in the field of mobile learning as they enhance students\u0026rsquo; achievement. For example, Kouhi and Rahmani (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) designed and developed a mobile application for teaching triple multiplication to young pre-school students and the finding revealed that students\u0026rsquo; achievement improved significantly after exposure to mobile learning app. Mohammed et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) developed an educational game to teach educational technology postgraduate students during the COVID-19 pandemic and findings revealed that students\u0026rsquo; performance significantly increased after exposure to mobile educational game to a very large extent. Demir and Akpinar (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) studied the effect of mobile learning technology on undergraduate students\u0026rsquo; achievement in graphic education and the findings revealed that the use of mobile application significantly improved the achievement of students. Han et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) designed and tested the effectiveness of an artificial intelligence mobile chatbot on college of nursing students\u0026rsquo; performance and the findings revealed that the use of artificial intelligence mobile chatbot improved the performance of students. Amasha et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) developed a java-based mobile application to teach primary school mathematics students and findings revealed that mobile application enhanced students\u0026rsquo; performance. Hsu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) designed and tested the effectiveness of an expert-decision-making mobile application to teach college students climate concepts and their findings revealed that students\u0026rsquo; performance significantly improved after exposure to the mobile application. Oyelere et al. (2017) designed, developed and evaluated a mobile application for teaching of computing education and the findings revealed the adoption of mobile application improved students\u0026rsquo; performance. Vazquez-Cano et al. (2022) designed and tested the effectiveness of mobile chatbot towards improving undergraduate students\u0026rsquo; punctuation skills and their findings revealed that students punctuation skills significantly improved after exposure to artificial intelligence mobile learning. Ali et al. (2024) developed a mobile application for teaching undergraduate English language and the findings revealed that the performance of students improved significantly after exposure to mobile application. Essel et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) designed and tested the effectiveness of a mobile virtual assistant on undergraduate students\u0026rsquo; programming skills and their findings revealed students\u0026rsquo; performance improved significantly. Mohammed et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) tested the effectiveness of students\u0026rsquo; performance in a microteaching course using Telegram and WhatsApp mobile applications and the findings revealed that students\u0026rsquo; achievement improved significantly after exposure to instruction using mobile learning. Falode et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) designed and evaluated a mobile application on educational technology students\u0026rsquo; achievement and the findings revealed that the adoption of mobile application in teaching and learning of educational technology significantly improved students\u0026rsquo; performance. Alkhateeb and Al-Duwairi (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) designed and tested the effectiveness of mobile application to teach geometry concepts to undergraduate students and their study revealed that students\u0026rsquo; performance increased significantly after exposure to mobile learning.\u003c/p\u003e \u003cp\u003eThe foregoing discuss revealed the usefulness of mobile learning towards enhancing students\u0026rsquo; performance in various disciplines. The studies we reviewed focused on educational technology (Mohammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Falode et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Demir \u0026amp; Akpinar, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), micro-teaching (Mohammed et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), programming and computing (Oyelere et al., 2017; Essel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); English and punctuation skills (Vazquez-Cano et al., 2022; Ali et al., 2024), nursing (Han et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mathematics (Amasha et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kouhani \u0026amp; Rahmani, 2023; Alkhateeb \u0026amp; Al-Duwairi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and climate change concepts (Hsu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the preceding studies revealed a unanimous finding in terms of the effectiveness of mobile learning towards enhancing students\u0026rsquo; performance in different fields, however, the studies reviewed were not conducted on Chemistry practical which therefore leaves a gap that needs to be filled. As a result, there is need for more studies to be conducted to close these gaps.\u003c/p\u003e \u003cp\u003eIn view of the preceding discussion, we formed the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHo\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the mean achievement scores of pre-service teachers taught practical chemistry before and after exposure to the mobile learning application\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMobile learning and gender differences in students\u0026rsquo; achievement\u003c/h2\u003e \u003cp\u003eGender as a moderating variable continues to play a role in terms of students\u0026rsquo; performance in mobile learning technology. For example, Hilao and Wichadee (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) checked whether there is any significant difference in male and female students\u0026rsquo; performance after exposure to mobile learning and the findings revealed that the students did not differ in their performance based on gender. The study of Falode et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) revealed that there was a significant difference in the achievement of male and female educational technology students exposed to mobile learning in favour of female students. The study of Sandu and Gide (2019) did not find any significant difference in terms of gender after exposure to mobile learning using a chatbot. The study of Daud et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) revealed a significant difference in the achievement of male and female students exposed to mobile learning. Additionally, the study of Essel et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found no significant difference in the achievement of male and female students after using mobile chatbot for instruction. Mohammed et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) checked gender difference in students\u0026rsquo; microteaching performance in a mobile learning using Telegram and WhatsApp and the findings revealed there was no significant difference in the achievement of male and female students.\u003c/p\u003e \u003cp\u003eIn line with the above studies reviewed, there seems to be no unanimous agreement regarding the role of gender in terms of students\u0026rsquo; performance during a mobile learning strategy. From the studies reviewed above, there are contradictory findings. For example, while some studies (e.g., Falode et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Daud et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found a significant difference in students\u0026rsquo; performance based on gender, some of the studies (e.g., Hilao \u0026amp; Wichadee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sandu \u0026amp; Gide, 2019; Essel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found no significant difference in terms of students\u0026rsquo; performance in a mobile learning. Therefore, this creates another gap that needs to be filled in order to add more literature in terms of gender differences in mobile learning.\u003c/p\u003e \u003cp\u003eThus, in view of this development above, we formed the next hypothesis:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHo\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the mean achievement scores of male and female pre-service teachers taught practical chemistry before and after exposure to the mobile learning application\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe study adopted a repeated measures design to evaluate the effectiveness of the mobile learning application whereby one group of pre-service students where studied over a period of 8 weeks in order to ascertain the periodic improvement in their achievement as a direct influence of the treatment. Edmonds and Kennedy (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed that repeated measures design permits researchers to acquire different data points over a long period with the main purpose of checking the observed variation in performance as a result of treatment or time. In this type of design, the various test conditions serve as a control for the different treatment settings. As a result, in this design, we subjected the participants to different testing conditions before and after the treatment over a long period of time to measure their periodic improvement in performance as a result of the mobile learning treatment issued to them. This design has been used in various studies (e.g., Mohammed et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Mohammed \u0026amp; Bello \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003ePurposive sampling technique was used to select 50 year-two pre-service teachers from Federal College of Education (FCE) Kontagora, Niger State Nigeria for the 2023/2024 academic session. The choice of year two pre-service teachers was employed because the course content covered was domiciled in year two chemistry course contents. As at the time of conducting this study, there were a total of 757 pre-service teachers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrument\u003c/h2\u003e \u003cp\u003eThe research instrument used in this study was the Practical Chemistry Achievement Test (PCAT) which consisted of 40-item multiple-choice questions drawn using Benjamin Bloom\u0026rsquo;s table of specification. We used Bloom\u0026rsquo;s table of specification in order to spread the entire questions across the various sub-levels of the cognitive domain of education as specified by Bloom. The test items thus covered the areas of knowledge, comprehension, application, analysis, synthesis and evaluation in order to thoroughly measure the students\u0026rsquo; cognitive understanding at the end of the treatment using mobile application. The instrument had two sections: section A and section B. Section A deals with the students\u0026rsquo; demographic data while section B consists of the 40-item multiple choice questions with options A-D, containing one correct answer and three distracters. In order to ascertain the item difficulty index of the test, we conducted an item analysis where a P-value of 0.36 was obtained, thus falling in accordance with the acceptable average difficulty range criteria of 0.30 to 0.49 (Hasan\u0026ccedil;ebi et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As scoring criteria, each correct answer was scored 2 marks while an incorrect answer was scored 0. This therefore means that the maximum score obtainable from the test was 40, while the minimum score was 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eValidity and Reliability\u003c/h2\u003e \u003cp\u003eThe instrument validation was done in two phases. First, we subjected the PCAT to expert validation in order to determine its validity by giving it to experts in the field of chemistry education and measurement and evaluation. These experts checked the face and content validity of the instrument and provided useful insights in the final draft of the instrument. Their corrections in terms of lengths of questions, eliminating easy options, chronological arrangement and language clarity were then implemented in the final production of the instrument used in the study. As for the mobile learning application, we first created a prototype that was given to experts in the field of instructional design, educational technology, computer science and software engineering in order the check the user interphase, selection pane, colour contrast and user friendliness during operation. Their observations were then corrected and the final mobile application was finally produced. In order to ascertain the reliability of the PCAT, copies of the instrument were given to different set of students that were within the population but outside the sample in a single administration and a reliability figure of 0.79 was obtained using Kuder-Richardson 20 formula. During the pilot test, some of the test items returned with a very weak value and were therefore removed in the final copy of the instrument to be used for the study.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eDevelopment of mobile learning application\u003c/h2\u003e \u003cp\u003eThe mobile application used in this study was designed using the ADDIE instructional design model as stated below:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnalysis\u003c/strong\u003e \u003cp\u003eThe analysis phase in developing the chemistry practical mobile learning application involves a comprehensive examination of need assessment and student profiling. The need assessment involves engaging with educators and students to identify challenges in chemistry practicals. This includes evaluating existing materials such as textbooks, lab manuals, and online resources to streamline the course development process. By focusing on learners' needs and the learning context, the analysis phase ensures the effective utilization of available resources and sets the foundation for the subsequent stages of course design and implementation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDesign\u003c/strong\u003e \u003cp\u003eDuring the design phase of the chemistry practical mobile learning application, we created a seamless and effective user experience through careful user interface (UI) design, a well-structured content layout, and the development of a functional prototype. A computer programmer and one of the researchers were involved in designing the package. The UI design includes selecting appropriate colors, typography, and icons that align with the subject while maintaining a consistent and visually appealing look. Ease of navigation is prioritized by ensuring users can easily access sections such as theory, experiments, quizzes, and supplementary resources. Choices regarding the programming language, operating system (Android), and memory size were made. Multimedia elements like videos and images are strategically embedded to enhance comprehension. Practice exercises and quizzes are integrated into relevant lessons to promote active learning.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDevelopment\u003c/strong\u003e \u003cp\u003eIn the development phase, the chemistry practical course content involves creating comprehensive instructional materials, organizing lesson sequences, and constructing relevant assessments to enhance learning outcomes. This phase considers factors such as the progression of difficulty, logical flow of concepts, and resource efficiency. Both formative assessments for ongoing feedback and summative assessments for final evaluation were formulated. The mobile application was developed using Kodular, a no-code platform for Android applications. Learning content, assessments, and YouTube video links were managed via an e-learning platform built with JavaScript, HTML, CSS, and PHP. User authentication protocols were integrated to validate identities during login and interactions. Authentication and authorization mechanisms were implemented to protect content and manage access securely. User registration and login functionalities were designed with advanced security measures. Self-assessment exercises were incorporated in each unit to gauge cognitive levels. Data integration was employed to ensure efficient communication between the app's front-end and back-end systems, with a robust database architecture established to store user profiles and track progress. An API was developed to facilitate seamless data exchange, maintaining consistency of user information and progress across devices.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplementation\u003c/strong\u003e \u003cp\u003eWe trained research assistants that helped the various students to install the mobile learning application. Additionally, an orientation session was conducted to familiarize students with the application's usage.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEvaluation\u003c/strong\u003e \u003cp\u003eThe evaluation phase of a chemistry practical course includes a post-test administered after the course, designed to assess their comprehension of practical chemistry topics taught with the application.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExperimental procedure\u003c/h2\u003e \u003cp\u003eEven though the study involved the use of human subject, ethical clearance was waived as per the Nigerian Ministry of Health\u0026rsquo;s code of research ethics (2007). We also obtained written informed consent in order to use the sampled students for the study where we also assured the participants of the confidentiality of their data for research purpose only. The students signed the consent form permitting the conduct of this study. The researchers issued two separate pre-test measures (pre-test 1 and pre-test 2) within two weeks interval prior to the commencement of the study in order to measure the students\u0026rsquo; entry behavior. Thereafter, the students were subjected to the treatment using the mobile learning application over a period of 8 weeks. The students interacted with the mobile application to learn the various chemistry practical concepts. For every week, the teacher introduced the concepts to be studied by the students where they learnt at their own pace once every week. The students planned their learning process in an individualized learning with the teacher serving as facilitator. The contents covered were balancing of redox reaction, redox titrations, and measurement of pH using indicators. After the 8-week treatment period, the researchers then administered the posttest measures (posttest 1 and posttest 2) within a period of two weeks. All other sources of information was blocked during the treatment to make sure the students concentrate and that data came from the mobile application only. In each of the test period, the students were required to provide answers within one hour.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMethod of analysis\u003c/h2\u003e \u003cp\u003eThe various results we obtained during the experiment were subjected to analysis using a mixed design repeated measures analysis of variance (RM-ANOVA) in order to compute the within subject effect of the various test conditions. We also computed the descriptive statistics using mean and standard deviation. Additionally, we conducted a normality test using Kolmogorov-Smirnov test and the result revealed that the data was normally distributed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We also computed the Skewness and Kurtosis of the data set and the values obtained falls within the acceptable range of \u0026plusmn;\u0026thinsp;2 and \u0026plusmn;\u0026thinsp;7 for Skewness and Kurtosis respectively (Hair et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; George \u0026amp; Mallery, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Bryne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) which therefore warrants the use of parametric statistics. In order to ensure that the assumption of sphericity was not violated as one of the basic assumptions of RM-ANOVA, we computed the Mauchly\u0026rsquo;s test of sphericity and the value revealed that P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 which means that the variance of all the within subject pairings of the different test conditions is equal. Sidak post-hoc test was also conducted to show the direction of the significant differences. The result was computed using SPSS software version 25.\u003c/p\u003e "},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean and standard deviation of students\u0026rsquo; achievement scores at different test occasions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e revealed the mean and standard deviation of students\u0026rsquo; achievement scores at different test period. The table revealed the students had M\u0026thinsp;=\u0026thinsp;47.02, SD\u0026thinsp;=\u0026thinsp;10.23, and M\u0026thinsp;=\u0026thinsp;48.34, SD\u0026thinsp;=\u0026thinsp;10.76 at pre-test 1 and pre-test 2 respectively. The table also revealed that the student obtained M\u0026thinsp;=\u0026thinsp;66.63, SD\u0026thinsp;=\u0026thinsp;9.01, and M\u0026thinsp;=\u0026thinsp;74.68, SD\u0026thinsp;=\u0026thinsp;9.19 at posttest1 and posttest 2 respectively. This reveals that the students\u0026rsquo; performance periodically improved in each case as a result of the treatment effect.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKolmogorov-Smirnov test of normality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.241\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.344\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.264\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.572\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eNot Significant, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e revealed the Kolmogorov-Smirnov normality test conducted to determine whether the data was normally distributed. The table revealed that P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all the different four test occasions. Therefore, the hypothetical assumption that the data set came from a normal distribution to warrant the use of parametric inferential statistics is hereby confirmed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean and standard deviation of male and female students\u0026rsquo; achievement at different test occasions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed the performance of male and female students at different test occasions after exposure to mobile learning application. The students had M\u0026thinsp;=\u0026thinsp;48.32, SD\u0026thinsp;=\u0026thinsp;10.47 and M\u0026thinsp;=\u0026thinsp;44.89, SD\u0026thinsp;=\u0026thinsp;9.71 at pre-test 1 for male and female respectively. It also revealed that M\u0026thinsp;=\u0026thinsp;47.06, SD\u0026thinsp;=\u0026thinsp;10.29 and M\u0026thinsp;=\u0026thinsp;50.42 and SD\u0026thinsp;=\u0026thinsp;11.45 at pre-test 2 for male and female respectively. Furthermore, the table also revealed that the students had M\u0026thinsp;=\u0026thinsp;64.61, SD\u0026thinsp;=\u0026thinsp;9.29 and M\u0026thinsp;=\u0026thinsp;66.63, SD\u0026thinsp;=\u0026thinsp;9.01 at posttest 1 for male and female respectively. It also revealed that M\u0026thinsp;=\u0026thinsp;72.67, SD\u0026thinsp;=\u0026thinsp;9.50 and M\u0026thinsp;=\u0026thinsp;77.94 and SD\u0026thinsp;=\u0026thinsp;7.22 at posttest 2 for male and female respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTesting of hypothesis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eHo\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the mean achievement scores of pre-service teachers taught practical chemistry before and after exposure to the mobile learning application\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRM-ANOVA of students\u0026rsquo; achievement in the various within subject test occasions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eηp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphericity Assumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28078.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9359.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenhouse-Geisser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28078.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9775.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuynh-Feldt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28078.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9359.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower-bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28078.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28078.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e109.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eError(Time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphericity Assumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12567.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenhouse-Geisser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12567.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuynh-Feldt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12567.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower-bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12567.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e256.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eSignificant P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals the RM-ANOVA of students\u0026rsquo; performance at different test occasions. The table revealed that F \u003csub\u003e(3,147)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;109.475, P\u0026thinsp;=\u0026thinsp;0.000 and Partial Eta Squared effect size (ηp\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;0.916. This shows that the null hypothesis is hereby rejected, which indicates that there was a significant difference in the students\u0026rsquo; performance at different test occasions. Thus, the use of mobile applications has a significant effect on students\u0026rsquo; performance. Additionally, the effect size (ηp\u003csup\u003e2\u003c/sup\u003e) of 0.916 means that 91.6% of the total variance in students\u0026rsquo; performance is linked to the use of mobile learning application. What this means is that the use of mobile learning application improved students\u0026rsquo; performance by 91.6% during the treatment period. Furthermore, the table also revealed that Mauchly\u0026rsquo;s test of sphericity for the within subject pairings was equally not significant (Mauchly (W)\u0026thinsp;=\u0026thinsp;0.938; P\u0026thinsp;=\u0026thinsp;0.691). This therefore means that the variance of all the within subject effect in all the different four test occasions is equal, thus satisfying the assumption of sphericity. In order to check the direction of the differences in significance, Sidak post-hoc pairwise comparison was conducted in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSidak post-hoc pairwise comparisons of students\u0026rsquo; performance at four different test occasions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(I) Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(J) Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Difference (I-J)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% Confidence Interval for Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-19.640\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-24.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-27.660\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-32.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-22.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-18.320\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-23.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-26.340\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-31.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-21.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosttest\u0026nbsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.640\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.320\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePosttest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.660\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePretest 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.340\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePosttest 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e* Significant P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e revealed the post-hoc pairwise comparison of students\u0026rsquo; performance in four different test occasions after exposure to mobile learning application. The table revealed that even though there was a significant difference between the pre-test and posttest scores of the students which indicates a periodic increase in performance as a result of the treatment, the significant difference between posttest 2 and pretest 1 was higher, followed by the significant difference between posttest 2 and pre-test 2.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHo\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e \u003cp\u003eThere is no significant difference in the mean achievement scores of male and female pre-service teachers taught practical chemistry before and after exposure to the mobile learning application\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRM-ANOVA of male and female students\u0026rsquo; performance at four different test occasions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType III Sum of Squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eηp\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTreatment * Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphericity Assumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.109\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenhouse-Geisser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuynh-Feldt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e171.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower-bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e515.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e515.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eError(Treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSphericity Assumed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12052.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreenhouse-Geisser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12052.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuynh-Feldt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12052.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower-bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12052.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e251.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eNot Significant P\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the RM-ANOVA of male and female students\u0026rsquo; performance at different test occasions after exposure to mobile learning application. The table revealed that F (3,144)\u0026thinsp;=\u0026thinsp;2.051, P\u0026thinsp;=\u0026thinsp;0.109, with an effect size of 0.21. This means that the hypothesis is hereby retained, thus signifying no significant difference in the mean achievement of male and female students after exposure to mobile learning. The partial eta squared effect size (ηp\u003csup\u003e2\u003c/sup\u003e) of 0.21 means that 21.0% of the total variance in the performance of male and female students after the treatment is linked to the mobile learning treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main aim of the study was to determine the effectiveness of a designed mobile application on pre-service teachers\u0026rsquo; performance in practical chemistry. After exposing the students to instruction using the mobile learning application, we discovered that the students\u0026rsquo; performance increased periodically in each of the test period as a result of the treatment. The null hypothesis was rejected which therefore means that there was indeed a significant difference in the performance of students exposed to mobile learning application. The finding of the study revealed that mobile learning application is directly linked to 91.6% of the total variance in students\u0026rsquo; performance after the treatment given the effect size of 0.916 that was recorded. This means that mobile learning application was responsible for improving students\u0026rsquo; performance by 91.6% which is categorized as a very high effect (Cohen, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Our finding could be that with a mobile application, students have the liberty to learn at their own pace and construct meaning out of what they are being taught. This improvement in performance is very possible given the various features of mobile learning which makes it suitable for learning like portability (Hameed et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), ubiquity (Wang, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), easing of learning process (Stecuła \u0026amp; Wolniak, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), improvement in communication and social interaction (Park et al., 2012), fostering personalized learning (Dalili Saleh et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), facilitating collaborative learning (Kumar Basak et al., 2018), ensuring autonomous learning (Elaish et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), mobility (Rohanai et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and facilitating blended learning opportunities (Buraimoh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, mobile learning provides students with different learning resources to learn from like video lectures and interactive learning contents which could appeal to many learning preferences of students as noted in this finding. Additionally, mobile learning provides an avenue for learning to take place anywhere, anytime which could help to increase student\u0026rsquo; curiosity towards learning. The finding agrees with the several empirical studies (e.g., Kouhi \u0026amp; Rahmani, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammed et al., 2024; Demir \u0026amp; Akpinar, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Amasha et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oyelere et al., 2017; Hsu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ali et al., 2024; Vazquez-Cano et al., 2022; Essel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Falode et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that we reviewed in this study. Our finding, therefore, provides an insight into the effectiveness of enhancing pre-service teachers\u0026rsquo; performance in practical chemistry amongst pre-service teachers from the Nigerian perspectives.\u003c/p\u003e \u003cp\u003eFurthermore, the finding of this study revealed that there was no significant difference in the performance of male and female students after exposure to mobile learning. This means that both male and female students performed almost equally since the difference was not significant even though male students achieved a higher mean. The result also revealed that 21.0% of the total variance in male and female students\u0026rsquo; performance is linked to mobile learning given the effect size of 0.21 that was noticed which, according to Cohen (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), is categorized as small. This finding, however, could be due to individual differences which now form the basis for further studies to be conducted to further add to the frontiers of knowledge in the field of gender differences in mobile learning. This finding agrees with a number of studies (e.g., Hilao \u0026amp; Wichadee, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sandu \u0026amp; Gide, 2019; Essel et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) we reviewed in this study. However, on the contrary, it disagrees with some studies (e.g., Falode et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Daud et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) we reviewed which revealed a significant difference based on gender. These inconsistent findings, however, could be due to individual differences. This study thus now forms the basis for further studies to be conducted in order to further add to the frontiers of knowledge in the field of gender differences in mobile learning.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications of findings for practice and research\u003c/h2\u003e \u003cp\u003eThe finding of this study has a number of implications for chemistry teachers, policy makers and other science education researchers in Nigeria. It underscores the fact that if mobile learning could be incorporated in teaching chemistry practical in Nigeria, students\u0026rsquo; performance could be enhanced for it has what it takes to eliminate poor infrastructure and inadequate equipment. This finding revealed that the use of mobile learning has what it takes to encourage personalized learning thereby enabling students to construct meaning out of the learning process at their own pace thus leading to increase in performance. From the research perspectives, this finding has equally added to the scarce literature in the areas of research and development of mobile application for teaching chemistry practical in Nigeria.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed that the use of mobile learning for teaching practical chemistry amongst pre-service teachers is very effective and it equally enhances students\u0026rsquo; performance thereby leading to the tackling of poor performance and eliminating challenges during practical. Therefore, lecturers should be taught on how to implement mobile learning in their teaching and learning. Additionally, workshops should be organized in order to teach lecturers how to incorporate mobile learning.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitation and further research directions\u003c/h2\u003e \u003cp\u003eThis study has a number of limitations that should be addressed. First of all, the study was conducted on pre-service teachers of colleges of education in a Nigerian college of education; thus, similar study should be conducted on other pre-service teachers especially at the university levels to further test the effectiveness of the mobile learning application. The study was limited to a few practical chemistry aspects and as such, generalizations could not be drawn; therefore, similar studies should be conducted in other areas of chemistry. The study sample was small and therefore the generalizability of the study could not be established; as a result, similar studies should be conducted using a larger population in order to further establish the effectiveness of the mobile learning application.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFMS: Conceptualisation, writing draft, editing and approval. IAM: Conceptualisation, writing draft, editing, methodology, editing, methodology, data analysis and approval. BMS: Conceptualisation, data collection, writing draft, development of mobile application and approval. FA: Project administration, supervision and approval AS: Technical support, software and approval.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors appreciate the students that participated in the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfyusisye, A., \u0026amp; Gakuba, E. (2022). The effect of the chemistry practicals on the academic performance of Ward Secondary School students in Momba District in Tanzania. 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Technology, Pedagogy and Education, 31 (3), 381\u0026ndash;398.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, M., Chen, Y., Zhang, S., Zhang, W., Li, Y., \u0026amp; Yang, S. (2022). Understanding mobile learning continuance from an online-cum-offline learning perspective: A SEM-neural network method. International Journal of Mobile Communication, 20, 105\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mobile learning, Chemistry Practical, Educational Technology, Pre-service teachers, ADDIE Model, ICT in Education, Chemistry Education","lastPublishedDoi":"10.21203/rs.3.rs-5071560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5071560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLack of proper laboratories and inadequate facilities have become some of the factors affecting the teaching of chemistry practical in Nigeria which therefore affects students\u0026rsquo; performance. While there were many studies conducted on the effectiveness of mobile learning, literature remains very scarce in the Nigerian context. In order to tackle this problem, using ADDIE instructional design model, we developed and tested the effectiveness of mobile learning towards improving college of education pre-service teachers\u0026rsquo; achievement in practical chemistry. The study adopted the use of repeated measures design whereby 50 pre-service teachers were purposively used in the study. A 40-item Practical Chemistry Achievement Test (PCAT) which was subjected to expert validation and reliability test was used to obtain data for the study. A normality test was conducted using Kolmogorov-Smirnov test and it was revealed that the data were normally distributed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.005). The students were given two pre-test and post-measures before and after the 8-week treatment period. The data were analyzed using mixed design repeated measures analysis of variance and we found that students\u0026rsquo; performance improved periodically with each testing period (F \u003csub\u003e(3,147)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;109.475, P\u0026thinsp;=\u0026thinsp;0.000 with an effect size of (ηp\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;0.916) after the treatment. The finding also revealed no significant differences in the performance of the students on the basis of gender. Our finding has some implications for lecturers, researchers and policy experts on the need to incorporate mobile learning in education. Our finding provides insights on the effectiveness of mobile learning towards enhancing students\u0026rsquo; chemistry practical knowledge.\u003c/p\u003e","manuscriptTitle":"Development and evaluation of mobile learning application for practical chemistry among pre-service teachers ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 13:40:21","doi":"10.21203/rs.3.rs-5071560/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-23T01:24:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-21T22:22:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-17T19:16:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109906104551931193879249109946272449757","date":"2024-10-13T09:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111970493430064463464679141538547828934","date":"2024-10-12T17:52:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240723017912506322406165171069578643251","date":"2024-10-11T09:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179115776715309680763257223387686157162","date":"2024-10-06T15:59:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-26T09:12:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-24T03:16:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-18T12:51:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2024-09-11T13:33:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9010550d-33f6-42f9-b545-7217e80d4edc","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-14T05:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-05 13:40:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5071560","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5071560","identity":"rs-5071560","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}