Creation of the Remote Learning Best Practices Satisfaction Survey

Creation of the Remote Learning Best Practices Satisfaction Survey | 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Article Creation of the Remote Learning Best Practices Satisfaction Survey Heather Hatchett This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6414745/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This paper describes the development of a new measure of student online course satisfaction called the Remote Learning Best Practices (RLBP) survey. “Remote” learning encompasses asynchronous online courses and those with a synchronous virtual component such as lectures via Zoom or Microsoft Teams. Unlike other instruments of its kind, RLBP creation was guided by a qualitative analysis of students’ reports of their course experiences. This unique methodology is to address that direct student input has not been a primary consideration in satisfaction survey development. Rational refinement of the initial item pool resulted in a 25-item scale with higher scores indicating stronger course satisfaction. RLBP factor composition favors the prominence of instructor presence and course design but downplays student interaction. Investigation of scale psychometric properties demonstrated a clear factor structure, strong reliability, and emerging evidence of construct validity. Results suggest the RLBP may add incremental value to a multi-measure approach of online course quality assessment. online course satisfaction survey online course satisfaction Introduction There has been an accelerating demand for online courses since COVID-19 restrictions (Simunich et al., 2024 ). Course quality has been of wide interest (Simunich et al., 2024 ), with a variety of assessment approaches evaluating teaching competencies and/or course design. Student input into course quality has mainly been participation in institution specific end-of-course satisfaction surveys (Medina et al., 2019 ). Below is a review of online course quality assessments and a proposal to use a combinative strategy that includes a satisfaction measure crafted from direct student input. The purpose of this paper is to describe the development of a new assessment of online course satisfaction created by qualitatively analyzing statements from online learners just after course completion. Methods of Evaluating Online Course Quality Instructional Competencies Instructional competency is an essential element of online course quality. Competency assessments are typically created by distilling best practices from peer reviewed scholarship, incorporating expert input, and/or combining elements of existing tools to create rubrics. As an example, Smith (2005) reviewed the literature and identified fifty-one instructor competencies. These competencies are categorized by importance either before, during, or after course delivery. He recommended organizing competencies into an “integrated or holistic approach” that acknowledges levels of teaching expertise: novice, experienced, and specialist (p. 6). In another effort to create guidelines for quality “distant” teaching, Darabi et al. (2006) first reviewed ten years of scholarship and identified twenty general competencies. Second, seasoned instructors wrote fifty-four task statements to describe the teaching activities per competency. Third, the task statements were rated according to importance and time investment. This resulted in a reduced list of seventeen tasks. When these instructional tasks were related back to the original competencies via a matrix - subject, technological, and social facilitation proficiencies emerged as primary. Grant and Dickson (2008) followed a similar approach to organizing instructional tasks by competencies. Their competency model is based on a literature review of the best practices of undergraduate education combined with theories of adult learning. It is a matrix of instructor competencies that includes knowledge of online pedagogy/format, course content development, understanding instructional design, and course management, all overlaying seven teaching best practices (“tasks”). The best practices are cooperation, time on tasks, communication, expectations, active learning, feedback, and respect for diversity. Both the competencies and tasks in turn are grouped by three larger themes - course design, instructor effectiveness, and course management - to provide a holistic approach to instructional excellence. The above competency models vary in number, type, and layout of competencies. In 2012, Hilke, recognizing the need to create an easily used quality rating tool, published an online teaching competency rubric based on a comprehensive review of existing standards and rubrics (as cited in Diehl, 2016). He identified seven areas of instructor competencies including institutional context, technologies, instructional design, pedagogy, assessment, social presence, and academic/professional expertise. Each domain breaks down into six or seven specific standards the instructor must meet. Diehl, in his 2016 literature review of online teaching competencies for Quality Matters, stated “. . .numerous articles lend support to Hilke’s Teaching Competency Rubric.” (p. 29). Course Design Diehl (2016) pointed out that many colleges and universities use in-house rubrics that solely address course design for online course evaluation. Two widely used rubrics across many higher education institutions are the Quality Matters (QM; https://www.qualitymatters.org/) and the OSCQR 4.0 (https://oscqr.suny.edu/). Quality Matters (QM; https://www.qualitymatters.org/) is a national leading assessment of web course design and uses a peer review process facilitated by a rubric based on online learning research. The QM rubric, updated every two to three years, helps online teaching experts to evaluate a course in relation to forty-four Specific Review Standards under eight General Standards. General standards include the following: Course Overview/Introduction, Learning Objectives, Assessment/Measurement, Instructional Materials, Course Activities/Learner Interaction, Course Technology, Learner support, and Accessibility/Usability. Points are awarded by standard with the greatest number assigned to 23 (6 th edition) or 22 (7 th edition) Essential Standards. Essential Standards must be met along with an 85% overall score for a course to attain QM certification (https://www.qualitymatters.org/qa-resources/rubric-standards/higher-ed-rubric). OSCQR was developed by State University of New York (SUNY) Online (https://explore.suny.edu/), a comprehensive web platform housing hundreds of online degrees in sixty-four SUNY institutions. The OSCQR-4 rubric has fifty review standards couched under six general standards: Course Overview/Information, Course Technology/Tools, Design/Layout, Content/Activities, Interaction, and Assessment/Feedback. It is non-evaluative, and rather than assigning points per standard, uses a four-point checklist that ranges from “Sufficiently Present” to “Major Revision” alongside an action plan per standard. It differs from Quality Matters by its formative focus and the lack of a certification goal. OSCQR may be used solely by an instructor or by multiple evaluators and is customizable with a “Not Applicable” option per standard. While there is significant similarity between the two rubrics, OSCQR organizes its content differently, and has more and better integrated accessibility standards. In sum, both the OSCQR and Quality Matters rubrics emphasize course design with instructor focus limited to interaction . Like with competency assessments, an issue is evaluating the extent of overlap (or common factors) between course design rating systems. For example, Debattista (2018) reviewed Quality Matter standards in combination with three in-house rubrics used by large universities. The individual rubrics included most of the combined criteria, but each used different point systems and rating scales. Debattista then created a comprehensive rubric that covered all aspects of each rating system. Like the QM rubric, Debattista’s tool uses specific standards in support of the main criteria. The main criteria are instructional design, course opening, assessment of learning, interaction/ community, instructional resources, learner support, technology design, course evaluation, course closing, and instructional design cycle. His rubric, unlike others that focus on one or the other, unites rating of course design (the main standards) with select instructor competencies that are mixed with design elements under specific standards - but it does not provide a point scale. In sum, course design and instructional competencies may be assessed in and of themselves, but some evaluative approaches do both by incorporating instructor skills in specific standards that support design fundamentals. Panning out, online course quality assessments address three main elements: organization and mechanics of the learning management environment/course design, course content (more rarely), and various teaching competencies, with these factors organized and weighed differently. Many universities and colleges use their own in-house assessments (e.g. McGahan et al., 2015). Quality assessments are usually completed by expert raters, and the relationships between assessment ratings and student success measures such as final grades, performance on summative exams, and course retention rates, are not typically investigated. Student Preferences and Satisfaction Some researchers have studied student preferences with implications for course satisfaction – the latter a common proxy of quality assessment. Five studies highlight the scope of student preferences for online course design and instructor practices. First, Sun et al. (2008) found that 66.1% of the variance of e-learner’s satisfaction can be explained by seven preference variables: learner computer anxiety, instructor attitude towards e-learning, course flexibility, e-learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments. Second, Gillingham and Molinari (2012) surveyed fifty-three undergraduate and graduate students in a Health Systems Management program concerning their perceptions of online courses. Students preferred the flexibility of the online modality, prompt feedback, pre-recorded video, and certain characteristics of the learning management system such as document sharing, assessment tools, and an electronic portfolio. Third, Vijay (2020) evaluated the preferences of 115 undergraduate medical students concerning their online classes. Students reported a preference for traditional teaching but appreciated the additional time online classes afforded. Video was the most preferred way of learning in online classes in comparison to audio, slides, or documents. Fourth, John et al. (2021) studied preferences for online learning components by surveying 72 Indian medical students. Sixty percent of the sample showed preference for recorded videos over synchronous sessions and the majority reported technical issues impeding learning. Fifth, Groton and Spadola (2022) investigated social work students’ preferences for online learning mechanisms using a focus group of 25 students. Seven preference themes emerged: visuals, interactive components, asynchronous structure, variety, accessibility, applied scenarios, and short/frequent assessments. In sum, two themes emerged across the student preference research – particular aspects of course design and skillful instructor behaviors. The combinative findings may be helpful for designing online courses consistent with what students perceive as helpful (or not) to their learning. For example, Placencia and Muljana (2019) made recommendations for folder organization, tabs, and LMS features based on students’ perceptions of course design that contributed to their learning. End-of-course satisfaction surveys are widely perceived as foundational to judging course quality (Bayrak et al., 2020). Yet, there isn’t much investigation into the facets of student course satisfaction. Diloreto et al (2022) created the 19-item Student Learning and Satisfaction in Online Learning Environments Instrument (SLS-OLE) based on a literature review, and a confirmatory factor analysis fit four domains: course structure/organization, learner interaction, student engagement, and instructor presence. A follow-up regression analysis found the best predictor of satisfaction and perceived learning was instructor presence. Bangert’s (2006) earlier analysis of an online course satisfaction measurement also yielded four factors: student-faculty interaction, active learning, time on task (self-regulated learning facilitated by course design), and cooperation among students. There are similarities with the SLS-OLE factors, with both models emphasizing quality relationships and course design, but the predictive power of Bangert’s factors isn’t flushed out. Does course satisfaction predict measures of student success such persistence, completion, and final grades? Two studies investigated the relationship between student satisfaction and continuance or completion rates. Al-Samarraie et al. (2018) found that information quality, task–technology fit, system quality, utility value, and usefulness were core factors for satisfaction with the persistent use of “e-learning” for both instructors and students. Focusing on completion, Herbert et al. (2006) studied the relationship between students’ satisfaction with institutional and instructional/course variables as assessed by the Noel-Levitz Priorities Survey for Online Learners™ (PSOL) (https://www.ruffalonl.com/enrollment-management-solutions/student-success/student-satisfaction-assessment/priorities-survey-for-online-learners/and retention). Their research demonstrated varied results across measures for completers and non-completers. However, successful completers were more satisfied with all aspects of their online course. The correlation between satisfaction and one measure of course success - final grades - is generally positive but exhibits a range of effect sizes (Svanum & Aigner, 2011). Benton and Cashin (2012) reviewed student ratings research from 1970 to 2010 and reported positive but modest correlations (.10 to .30) with final grades. The investigators found similar ratings whether a course was face-to-face or online. In another study examining the relationship between satisfaction and course grades in a sample of 4290 online students, Chitkushev et al. (2014) found course satisfaction was strongly correlated with instructor satisfaction but weakly associated with final grades. In combination, the research suggests that factors other than grades, such as course design, course difficulty, and instructor characteristics, play significant roles in course satisfaction. Purpose of this Study – Use of Student Self-Reports for Survey Development Al-Samarraie (2017) recommended exploring the preferences of a target population before designing an online course. An easily used measure of online course satisfaction – based on direct accounts of course perceptions – would add incremental value in combination with course design rubrics for comprehensive quality assessments of new and established courses. Except direct and timely student input hasn’t been factored in satisfaction survey development. One project assessed what students thought of course design assessment items. Ralston-Berg et al. (2015) invited a large sample of college students ( n = 3,160) to rate the importance of Quality Matters standards (with items restated from a student perspective). The researchers found that, generally, students valued the QM criteria, but they did not value peer interaction in proportion to its scoring weight. Ehlers (2018) expounds that “e-learning has to be based on the learner” (p. 1) and argues that quality of a learning process is a “co-production” between the learner and learning environment. The current study applies this idea by partnering with students in crafting a satisfaction measure. This paper presents the results of a three-part project to develop a student-centered satisfaction instrument. The project started with investigation that began soon after in-person general education courses at a Midwest community college had been converted to an online format in temporary response to the COVID-19 pandemic. For part one, students were asked what was helpful and what would have improved their online learning experience. Qualitative analysis of the students’ written statements prefaced the writing of an item pool based on learner preferences just after course completion. For parts two and three, the psychometric properties of the new assessment, called the Remote Learning Best Practices survey (RLBP; called “remote” with goal of usability across virtual learning modalities), was investigated using two student samples. This included its dimensionality, internal consistency reliability, and RLBP score relationships with predicted final course grades, GPA, and two summative measures of satisfaction. Part One Methods and Results Data was collected in asynchronous and synchronous online courses offered by the Social and Behavioral Sciences Department at an open admission, midwestern community and technical college. The institution’s enrollment varies, with 7,000 to 9,700 students pursuing over one-hundred degree and certificate pathways. Student composition is 56.8% White, 24% African American, 4% Multi-Racial, 3.4% Hispanic or Latino, 3.8% Asian, <1% American Indian/Alaska Native/Pacific Islander, and 7.8% Unknown. These percentages are based on an institutional student demographic five-year trend report (2018-2022). Methods The qualitative data used to create the RLBP item pool was produced by 120 students enrolled in four introductory psychology and six introductory sociology online courses in the summer of 2020. Students completed an anonymous survey in the Blackboard Learning Management System about course modality preferences at the end of the semester. Neither student or instructor identifying information (including course section numbers) nor demographic data was requested to simplify data collection using the Blackboard survey tool. Instructors determined via gradebook indicators if students completed the survey to grant extra credit, with results available in batch format only. Results were sent to the primary investigator who combined them into a larger file for analysis. Two questions concerning student perceptions were embedded in the larger survey: Please tell us about 2-3 things that were good or helpful to your learning successfully in a web class format (any format) this summer. Please write a brief explanation for each. Please tell us 2-3 things that would have improved your web learning experience this summer. Please give a brief explanation of each. Responses were collated into thirty-five pages of statements. Eight-hundred and forty-four statements were produced with 502 in response to question one and 342 in response to question two. NVivo was used to identify patterns in the writing and similar themes emerged per question. For question one, five themes were identified as helpful to the learning experience: course design/content (42% of statements), instructor communication and interpersonal factors (19%), instructor behaviors (16%), aspects of the learning experience that saved time (this theme conflated with course design with exception of a few statements (12%), and learner factors (behaviors or situations impacting learning; 7%). Four percent of statements in response to question one were not relevant to the course experience. For question two, four themes were identified as problematic for the learning experience: course design/content (48% of statements), instructor communication and interpersonal factors (20%), instructor behaviors (11%), learner factors (11%), and technology (7%). Two percent of statements in response to question two did not relate to the course experience. The final 25-item “Remote Learning Best Practices Inventory” (RLBP) was created by writing items representative of students’ commentary organized by the two largest themes: course design/content and instructor behaviors; item wording reflected the sub-themes (codes) under the main themes. “Learner factors,” such as problems with self-discipline, motivation, and time management, although an issue related to online course satisfaction, were not represented in the final item pool as the purpose was to develop a tool useful for instructors to improve course quality. Part Two Methods and Results Methods The second project examined the relationship between RLBP total scores, a single item measure of course satisfaction, predicted final course grades, and predicted GPA. The internal consistency reliability and dimensionality of the new assessment was also investigated. Data was collected from 514 participants in the last two weeks of the fall 2022 semester. Students in web introductory general education classes representing seven disciplinary areas were sent a Survey Monkey link via their class instructors. The link was provided to instructors by the primary investigator, and recipients (students/instructors) did not have access to survey responses. Students demonstrated their participation by taking a screen shot of a statement on the survey: “Proof of completion for Remote Learning Survey” and later sending it to their instructors for extra credit. Student, instructor, and class section identifying information were not included in data collection to safeguard confidentiality. The survey included standard demographic questions, a question about the predicted grade point average (after semester’s conclusion), and a question about predicted final course grade. Respondents completed the 25 RLBP items and were asked about their overall course satisfaction via a single item measure. Out of 514 survey participants, 62 reported completing it in more than one class. One of their data sets was removed to avoid double entries. The college has a large dual credit population taking fall-spring semester courses and 87 survey respondents reported to be under 18. Once this data was removed to improve generalizability, and, after accounting for overlap between “under 18” and the multiple completer participants, 370 data entries were available for analysis. Students surveyed included 10 criminal justice, two geography, 41 history, 78 economics, 11 political science, 128 psychology, and 100 sociology students in introductory (100-level) general education classes. Participants ranged widely in their disciplinary pathways including business ( n = 61), education ( n = 15), engineering ( n = 16), food sciences ( n = 5), health careers ( n = 141), information technology ( n = 18), helping professions ( n = 25), humanities/sciences ( n = 58), public safety ( n = 2), technical or skill based ( n = 5), and other ( n = 23), with one participant not reporting. Survey respondents consisted of individuals who identified as female ( n = 270), male ( n = 89), non-binary ( n = 9), transgendered ( n = 1), and other ( n = 1). Reported ethnicities included White ( n = 232), Black ( n = 97), Hispanic/Latino ( n = 16), Asian/Asian American ( n = 13), American Indian/Alaska Native (n = 2), and Other ( n = 10). The sample age range was 18 to 24 ( n = 174), 25 to 34, ( n = 122) 35 to 44 ( n = 59), 45 to 54 ( n = 11), and 55 to 64 ( n = 4). Results Final grades were reported as 1 (A), 2 (B), 3 (C), 4 (D), 5 (F), and 6 (incomplete). GPA was reported as 1 (less than 2.0), 2 (2.0), 3 (2.5), 4 (3.0), 5 (3.5), and 6 (4.0). Course satisfaction was reported as 1 ( very satisfied ), 2 ( satisfied ), 3 ( neither satisfied nor dissatisfied ), 4 ( dissatisfied ), and 5 ( very dissatisfied ). The twenty-five RLBP items were rated 1 ( strongly agree ), 2 ( agree) , 3 ( neither agree nor disagree ), 4 ( disagree ), and 5 ( strongly disagree ). Items measuring final grades, course satisfaction, and the RLBP were reverse coded prior to analysis for interpretive ease. There were no significant differences in average scores for expected final course grades, expected GPA, course satisfaction, and RLBP scores across gender and ethnic identification categories. Because of this, analyses per disciplinary area – with some having few students - was not conducted. Most students expected a final course grade between an A and B and their GPA at semester’s end to be 3 to 3.5. Average course satisfaction was between “very satisfied” and “satisfied”, and the average RLBP score was 108.93 with a range of 36 to 125. Table I Descriptive Statistics N Minimum Maximum Mean Std. Deviation Final grade 370 1 6 5.25 .81 GPA 370 1 6 4.56 1.14 Satisfaction 369 1 5 4.39 .83 RLBP total 358 36 125 108.93 15.10 RLBP total scores were correlated with the single item measure of course satisfaction. Spearman’s rho evidenced a strong correlation ( r = .79, p < .001). There was a moderate correlation between RLBP scores and predicted final course grade ( r = .27, p < .001) but no significant relationship between RLBP scores and predicted GPA at semester’s end ( r = .09, p = .11). Exploratory Factor Analysis and Internal Consistency Reliability A principal component analysis was used to investigate the dimensionality of the 25 RLBP items. Initial analysis showed that most items were not highly skewed with values in an acceptable range (.62 to 2.08; George & Mallery, 2010). Kurtosis values ranged from -.37 to 5.6, also in an acceptable range (Byrne, 2010). The Kaiser-Meyer-Olkin (KMO) value of .95 and Bartlett’s Test of Sphericity (χ² 6945.18, p < .001) indicated the data was suitable for factor analysis. Initial factor extraction resulted in three factors with eigenvalues greater than one. The first counted for over half of the variance. As the primary intent of this study was scale development, several principal component analyses were conducted, to yield the most interpretable results. Four criteria guided the number of factors to rotate in stage two of the analysis: the hypothesis that the RLBP was multidimensional, the screen test, the absolute and relative values of eigenvalues computed in stage one, and the interpretability of the rotated solution (Green & Salkind, 2014). More specifically, due to three factors meeting the eigenvalue- greater-than-one cutoff criteria in stage one (Green and Salkind, 2014), and the large disparity between the first and latter eigenvalues, two instead of three factors were rotated using a varimax procedure for the clearest solution. This produced two interpretable dimensions labeled “Helpfulness of Course Experience” (59.94% of the variance) and “Ease of Course Experience” (33.66% of the variance). All items loaded on a single factor despite two having less relative differentiation. These items are “This course has graded work that promotes learning outcomes rather than busy work” and “The course uses instructional materials that are helpful for learning” (see tables III and IV). The coefficient alpha internal reliability estimate of RLBP items was .96. The estimate for factor one was .95 and for factor two was .92. Part Three Methods and Results Methods The third project replicated part two procedures with addition of a summative measure of Quality Matters (QM) Higher Education Rubric, Sixth Edition Essential Standard items (labeled QM_ES). Data was collected from 135 participants at the end of the Spring 2023 semester. The 23 QM Essential Standard items were presented after the RLBP in the online survey. Six of these items were slightly re-worded (with content unaltered) for better applicability to student raters. For example, item 2.2 reads “The module/unit learning objectives or competencies describe outcomes that are measurable”. The word “weekly” was added after “unit” to reflect the many web courses at the college with weekly objectives. Another example, item 7.2 reads “Course instructions articulate or link to the institution’s accessibility policies and services”. “Or the syllabus” was added after “instructions” as the college syllabus template includes a link to these policies. Data from 97 participants was available for analysis after removing double entries and dual credit students under 18. Students surveyed included those in introductory communications ( n = 19), economics ( n = 18), history ( n = 24), and sociology ( n = 34) general education classes with two participants not reporting class type. Represented disciplinary pathways included business ( n = 17), education ( n = 2), engineering ( n = 4), food sciences ( n = 1), health careers ( n = 30), information technology ( n = 5), helping professions ( n = 8), humanities/sciences ( n = 21), public safety ( n = 4), technical or skill based ( n = 2), and other ( n = 3). Participants consisted of individuals who identified as female ( n = 69), male ( n = 22), non-binary ( n = 5), and transgendered ( n = 1). Reported ethnicities included White ( n = 64), Black ( n = 24), and Asian/Asian American ( n = 9). The sample age range was 18 to 24 ( n = 54), 25 to 34 ( n = 27), 35 to 44 ( n = 10), and 45 to 54 years ( n = 6). Results Expected final course grades, expected GPA, course satisfaction, RLBP and QM_ES total score averages were computed across demographic groups with no supported reason to investigate between group differences (considering part two). Dimensionality analysis was not repeated due to the smaller sample size. The QM Essential Standard items were rated the same as RLBP items: 1 ( strongly agree ), 2 ( agree ), 3 ( neither agree nor disagree ), 4 ( disagree ), and 5 ( strongly disagree ) and reverse scored for interpretive ease. Most students expected a final course grade between A and B and their GPA at the end of the semester to be between 3 and 3.5. Average course satisfaction was between “neither satisfied nor dissatisfied” and “satisfied” and average RLBP scores = 99.58 with a range of 60 to 125. There was a strong correlation between RLBP scores and course satisfaction ( r = .83, p < .001). There was a moderate, positive correlation between RLBP scores and predicted final course grades ( r = .26, p < .01) and no significant relationship between RLBP and predicted GPAs at semester’s end ( r = - .02, p = .85). These findings are the same size and direction as in the previous analyses. QM_ES and RLBP scores demonstrated a robust relationship ( r = .89, p < .001). There was also a strong relationship between QM_ES scores and course satisfaction ( r = .79, p < .001). QM_ES scores and predicted final course grade demonstrated a small, positive relationship ( r = .23, p = .03). There was no significant relationship between QM_ES and expected GPA ( r = -.05, p = .67). Table V Correlations RLBP QM_ES Satisf. Grade GPA RLBP 1.000 .887 ** .831 ** .256 * -.019 . .013 .854 N 93 92 93 93 92 QM_ES .887 ** 1.000 .778 ** .225 * -.045 . .029 .670 N 92 94 94 94 93 Satisf. .831 ** .778 ** 1.000 .315 ** -.014 . .002 .896 N 93 94 97 97 96 Grade .256 * .225 * .315 ** 1.000 .573 ** .013 .029 .002 N 93 94 97 97 96 -.019 -.045 -.014 .573 ** 1.000 .854 .670 .896 . N 92 93 96 96 96 Note. ** p < 0.01; * p < 0.05. QM_ES = Quality Matters Essential Standards; Satisf. = course satisfaction; Grade = final course grade. Discussion This project involved the creation of a novel assessment of online course satisfaction using the self-report of students’ preferences. Unlike other student satisfaction measures, the users of the product drove survey creation. Our qualitative data revealed similar primary themes as identified in the e-learning preferences literature: course design and instructor behaviors. However, dimensionality analysis also suggested helpfulness and ease of learning experiences – both design and instructor related - were dominant factors overlaying RLBP items. The RLBP factor composition is generally supportive of the satisfaction dimensions evidenced by DiLoreto et al. (2022) but with less emphasis on learner interaction. Twelve RLBP items (48%) tap into the instructor’s presence but just one focuses on peer interactions. This is consistent with Ralston-Berg et al.’s (2015) discovery that students valued classmate relations less than as credited by a widely used quality assessment. It’s possible that relational instructional techniques are hard to apply in an online modality. But it is important to note that peer interactions take many forms. It’s likely that not all online learner interactions are valued equally, and that some types, such as topical engagement via guided discussions, multiple participant gamified competitions, or use of virtual study groups, are palatable or even enjoyed. Initial validation results revealed budding construct validity for the RLBP survey. Patterns of correlation followed expected directions with robust associations between overall course satisfaction, a summative measure of QM Higher Education Rubric Essential Standard items, and RLBP survey scores. Predicted final course grades and RLBP scores were moderately correlated, aligned with Benton and Cashin (2012) forty-year review of the relationship between grades and course satisfaction. The correlation between predicted overall GPA and RLBP scores was nonsignificant, hardly surprising, as students were rating satisfaction with just the single class of survey administration. Limitations Data for part one of the project was collected shortly after all humanities and most of the science courses at the college – if not already online - were pivoted to the web modality due to COVID-19 restrictions. Thus, some study participants in project one may have been in newly transitioned classes while others enrolled in established online sections. These courses were also a mix of asynchronous and synchronous classes with synchronous a novel learning modality. Both issues may have resulted in students being more critical of their learning experiences in comparison to a more typical semester. If true, it is impossible to know if this produced heightened variance in student preferences, possibly limiting generalizability. On the other hand, this atypical situation likely magnified common problems in online courses. Our students’ perceptions of problematic and helpful course experiences were similar to the general and specific online course preferences identified by previous research. But a shortcoming of the current study is our initial participants’ demographic characteristics were not collected. Thus, it could not be definitively determined if student composition was similar across the three samples, although it was unlikely to be widely disparate given enrollment patterns. Recommendations for Future Research The RLBP evidenced psychometric promise in the present study by demonstrating a sensible pattern of correlations with overall course satisfaction (albeit a one-item measure), final course grades, GPA, and QM Essential Standards. An interesting follow-up would be to investigate the relationships between RLBP scores and a diverse assortment of student satisfaction and course success measures. These might include end-of-course satisfaction surveys or summative knowledge assessments. There are several considerations for prospective investigation of RLBP dimensionality. First, it is possible that the RLBP survey is best conceptualized as one or even three-dimensional inventory; factor analysis decisions are partly subjective. The RLBP structural analysis should be explored with a varied number of factors and rotation types , both orthogonal and oblique, before deciding on a definitive solution. Second, high RLBP scores on average introduced a degree of range restriction, which may impact structure generalizability. Third, if part one of the study is replicated, dimensional exploration of fresh item pools would determine if our students’ preference buckets are hardy. Fourth, replication of the item generation procedure with students enrolled in established web classes (using a particular definition, such as courses that have passed an internal quality review or those taught for several years) may flush out subtle concerns specific to developed classes. The RLBP demonstrated excellent internal consistency reliability. In fact, its reliability is so robust, it opens to consideration whether the RLBP would be improved if select items were eliminated and replaced by others measuring evidence-based usability characteristics. As an example, design and content usability for diverse learners, including those with disabilities, are essential elements of course excellence as assessed by Quality Matters. Another example is adding an item addressing technology adequacy (such as consistent connectivity), a concern mentioned by some students in the item generation sample, but with insufficient frequency for scale representation. Adequate technology may vary enough between institutions to warrant an item when measuring online course satisfaction. Despite these potential limitations, it is probable that our students’ perceptions of helpful and problematic learning experiences capture common strengths and pitfalls in online courses. Our students’ preferences generally supported prior research evidencing quality course design and pro-student instructor behaviors/attitudes as dominant factors subsuming specific preferencesdriving satisfaction. More granularly, our students preferred frequent assessments (“graded work”), videos, interesting learning activities, and student-centered instructor interactions. RLBP items are actionable, and the scale can be used for a mid- or end-of-semester check of student satisfaction. Nonetheless, whether the RLBP is a valid and pragmatic assessment of student satisfaction, based on reliable preferences for students at large, is a topic of future investigation. It is hoped that a satisfaction survey like the RLBP, based on student voices in real time, will be routinely incorporated into a multiple measure approach of online course quality assessment. Declarations Funding declaration: no funding was received for this project and the author has no financial interests to disclose. Clinical Trial Number: not applicable. Consent to Publish declaration: not applicable. Consent to Participate declaration: not applicable. Human Ethics and Consent to Participate declarations: not applicable. University of Cincinnati Institutional Review Board, protocol: 2020-0538 The IRB classified the research as “Non-Human Research Determination” which means exempt. The IRB reviewed the study submission and determined that the proposed activity is not research involving human subjects as defined by DHHS, DOJ and FDA regulations. Author Contribution This paper was written by a single author. Data Availability Data Availability Declaration The data that supports the findings of this study are available upon reasonable request from the author. Please contact the author at heather.hatchett@cincinnatistate to request the data. When doing so, please provide your institutional affiliation and purpose for request. References Bangert, A. W. (2006). Identifying factors underlying the quality of online teaching effectiveness: An exploratory study. Journal of Computing in Higher Education , 17 (2), 79–99. https://doi.org/10.1007/bf03032699 Al-Samarraie, H., Teng, B. K., Alzahrani, A. I., & Alalwan, N. (2018). E-learning continuance satisfaction in higher education: A unified perspective from instructors and students. Studies in Higher Education , 43 (11), 2003–2019. https://doi.org/10.1080/03075079.2017.1298088 Bayrak, F., Tibi, M. H., & Altun, A. (2020). Development of online course satisfaction scale. Turkish Online Journal of Distance Education , 21 (4), 110–123. https://doi.org/10.17718/tojde.803378 Benton, Stephen L., & Cashin, W. E. (2012). "Student ratings of teaching: A summary of research and literature." IDEA paper 50 (2012): 1-20. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (multivariate applications series). New York: Taylor & Francis Group , 396 (1), 7384. Chitkushev, L., Vodenska, I., & Zlateva, T. (2014). Digital learning impact factors: Student satisfaction and performance in online courses. International Journal of Information and Education Technology , 4 (4), 356–359. https://doi.org/10.7763/ijiet.2014.v4.429 Darabi, A. A., Sikorski, E. G., & Harvey, R. B. (2006). Validated competencies for distance teaching. Distance Education , 27 (1), 105-122. https://doi.org/10.1080/01587910600654809 Debattista, M. (2018). A comprehensive rubric for instructional design in e-learning. International Journal of Information and Learning Technology , 35 (2), 93–104. https://doi.org/10.1108/ijilt-09-2017-0092 Diehl, W. C. (2016). Online instructor and teaching competencies: Literature review for Quality Matters. Quality Matters . Diloreto, M., Gray, J., & Schutts, J. (2022). Student Satisfaction and Perceived Learning in Online Learning Environments: An Instrument Development and Validation Study. Education Leadership Review , 23 (1), 115. https://files.eric.ed.gov/fulltext/EJ1380107.pdf Ehlers, Ulf-D. (2018). Quality in e-Learning from a Learner’s Perspective. Distances et Médiations Des Savoirs , 23 . https://doi.org/10.4000/dms.2707 Gillingham, M., & Molinari, C. (2012). Online courses: Student preferences survey. Journal of Online Learning Research and Practice , 1 (1). https://doi.org/10.18278/il.1.1.4 Grant, M. R., & Dickson, V. (2008). Matrix on virtual teaching: A competency-based model for faculty development. Adult Education Research Conference . https://newprairiepress.org/aerc/2008/papers/19 Green, S.B. & Salkind, N.J. (2014) Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (7th ed.). Prentice Hall, Upper Saddle River. Groton, D. B., & Spadola, C. E. (2022). Variability, visuals, and interaction: Online learning recommendations from social work students. Social Work Education , 41 (2), 157–165. https://doi.org/10.1080/02615479.2020.1806997 Herbert, M. (2006). Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration , 9 (4), 300-317. https://ojdla.com/archive/winter94/herbert94.pdf John, M. R., Sharma, D. K., Poonuraparampil, J. A., & Konuri, V. K. (2021). A study on the advantages and disadvantages of the online teaching program conducted in the Department of Anatomy, AIIMS, Raipur–Students' perspective. National Journal of Clinical Anatomy , 10 (1), 10-16. https://doi.org/10.4103/njca.njca_12_20 McGahan, S. J., Jackson, C. M., & Premer, K. (2015). Online course quality assurance: Development of a quality checklist. InSight: A Journal of Scholarly Teaching , 10 , 126- 140. https://files.eric.ed.gov/fulltext/EJ1074062.pdf Medina, M. S., Smith, W. T., Kolluru, S., Sheaffer, E. A., & DiVall, M. (2019). A Review of Strategies for Designing, Administering, and Using Student Ratings of Instruction. American Journal of Pharmaceutical Education , 83 (5). https://doi.org/10.5688/ajpe7177 Placencia, G., & Muljana, P. S. (2019, April). The effects of online course design on student course satisfaction. In 2019 Pacific Southwest Section Meeting . Ralston-Berg, P., Buckenmeyer, J., Barczyk, C., & Hixon, E. (2015). Students’ Perceptions of Online Course Quality: How Do They Measure Up to the Research? Internet Learning . https://doi.org/10.18278/il.4.1.2 Simunich, B., Garrett, R., Fredericksen, E. E., McCormack, M., Robert, J., & Ubell, R. (2024). CHLOE 9: Strategy Shift: Institutions respond to sustained online demand, the changing landscape of online education, 2024. Retrieved from the Quality Matters website: https://qualitymatters.org/qa-resources/resource-center/articles- resources/CHLOE-9-report-2024 Smith, T. C. (2005). Fifty-one competencies for online instruction. The Journal of Educators Online , 2 (2), 1-18. http://www.savie.qc.ca/CampusVirtuel/Upload/Fichiers/fifty%20one%20competencies.p df Svanum, S., & Aigner, C. (2011). The influences of course effort, mastery and performance goals, grade expectancies, and earned course grades on student ratings of course satisfaction. British Journal of Educational Psychology , 81 (4), 667– 679. https://doi.org/10.1111/j.2044-8279.2010.02011.x Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives successful e- Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education , 50 (4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007 Vijay, R. (2020). Comparative evaluation of COVID-19 pandemic enforced online teaching versus traditional teaching from point of view of medical students. International Journal of Basic & Clinical Pharmacology , 10 (1), 36-43. https://doi.org/10.18203/2319- 2003.ijbcp20205535 Additional Declarations No competing interests reported. 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Course quality has been of wide interest (Simunich et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with a variety of assessment approaches evaluating teaching competencies and/or course design. Student input into course quality has mainly been participation in institution specific end-of-course satisfaction surveys (Medina et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Below is a review of online course quality assessments and a proposal to use a combinative strategy that includes a satisfaction measure crafted from direct student input. The purpose of this paper is to describe the development of a new assessment of online course satisfaction created by qualitatively analyzing statements from online learners just after course completion.\u003c/p\u003e"},{"header":"Methods of Evaluating Online Course Quality","content":"\u003cp\u003e\u003cstrong\u003eInstructional Competencies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInstructional competency is an essential element of online course quality. Competency assessments are typically created by distilling best practices from peer reviewed scholarship, incorporating expert input, and/or combining elements of existing tools to create rubrics. As an example, Smith (2005) reviewed the literature and identified fifty-one instructor competencies. These competencies are categorized by importance either before, during, or after course delivery. He recommended organizing competencies into an “integrated or holistic approach” that acknowledges levels of teaching expertise: novice, experienced, and specialist (p. 6). In another effort to create guidelines for quality “distant” teaching, Darabi et al. (2006) first reviewed ten years of scholarship and identified twenty general competencies. Second, seasoned instructors wrote fifty-four task statements to describe the teaching activities per competency. Third, the task statements were rated according to importance and time investment. This resulted in a reduced list of seventeen tasks. When these instructional tasks were related back to the original competencies via a matrix - subject, technological, and social facilitation proficiencies emerged as primary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGrant and Dickson (2008) followed a similar approach to organizing instructional tasks by competencies. Their competency model is based on a literature review of the best practices of undergraduate education combined with theories of adult learning. It is a matrix of instructor competencies that includes knowledge of online pedagogy/format, course content development, understanding instructional design, and course management, all overlaying seven teaching best practices (“tasks”). The best practices are cooperation, time on tasks, communication, expectations, active learning, feedback, and respect for diversity. Both the competencies and tasks in turn are grouped by three larger themes - course design, instructor effectiveness, and course management - to provide a holistic approach to instructional excellence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above competency models vary in number, type, and layout of competencies. In 2012, Hilke, recognizing the need to create an easily used quality rating tool, published an online teaching competency rubric based on a comprehensive review of existing standards and rubrics (as cited in Diehl, 2016). He identified seven areas of instructor competencies including institutional context, technologies, instructional design, pedagogy, assessment, social presence, and academic/professional expertise. Each domain breaks down into six or seven specific standards the instructor must meet. Diehl, in his 2016 literature review of online teaching competencies for Quality Matters, stated “. . .numerous articles lend support to Hilke’s Teaching Competency Rubric.” (p. 29).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCourse Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiehl (2016) pointed out that many colleges and universities use in-house rubrics that solely address course design for online course evaluation. Two widely used rubrics across many higher education institutions are the Quality Matters (QM; https://www.qualitymatters.org/) and the OSCQR 4.0 (https://oscqr.suny.edu/).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eQuality Matters (QM; https://www.qualitymatters.org/) is a national leading assessment of web course design and uses a peer review process facilitated by a rubric based on online learning research. The QM rubric, updated every two to three years, helps online teaching experts to evaluate a course in relation to forty-four Specific Review Standards under eight General Standards. General standards include the following: Course Overview/Introduction, Learning Objectives, Assessment/Measurement, Instructional Materials, Course Activities/Learner Interaction, Course Technology, Learner support, and Accessibility/Usability. Points are awarded by standard with the greatest number assigned to 23 (6\u003csup\u003eth\u003c/sup\u003e edition) or 22 (7\u003csup\u003eth\u003c/sup\u003e edition) Essential Standards. Essential Standards must be met along with an 85% overall score for a course to attain QM certification (https://www.qualitymatters.org/qa-resources/rubric-standards/higher-ed-rubric).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOSCQR was developed by State University of New York (SUNY) Online (https://explore.suny.edu/), a comprehensive web platform housing hundreds of online degrees in sixty-four SUNY institutions. The OSCQR-4 rubric has fifty review standards couched under six general standards: Course Overview/Information, Course Technology/Tools, Design/Layout, Content/Activities, Interaction, and Assessment/Feedback. It is non-evaluative, and rather than assigning points per standard, uses a four-point checklist that ranges from “Sufficiently Present” to “Major Revision” alongside an action plan per standard. It differs from Quality Matters by its formative focus and the lack of a certification goal. OSCQR may be used solely by an instructor or by multiple evaluators and is customizable with a “Not Applicable” option per standard. While there is significant similarity between the two rubrics, OSCQR organizes its content differently, and has more and better integrated accessibility standards. In sum, both the OSCQR and Quality Matters rubrics emphasize course design with instructor focus limited to interaction\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike with competency assessments, an issue is evaluating the extent of overlap (or common factors) between course design rating systems. For example, Debattista (2018) reviewed Quality Matter standards in combination with three in-house rubrics used by large universities. The individual rubrics included most of the combined criteria, but each used different point systems and rating scales. Debattista then created a comprehensive rubric that covered all aspects of each rating system. Like the QM rubric, Debattista’s tool uses specific standards in support of the main criteria. The main criteria are instructional design, course opening, assessment of learning, interaction/ community, instructional resources, learner support, technology design, course evaluation, course closing, and instructional design cycle. His rubric, unlike others that focus on one or the other, unites rating of course design (the main standards) with select instructor competencies that are mixed with design elements under specific standards - but it does not provide a point scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn sum, course design and instructional competencies may be assessed in and of themselves, but some evaluative approaches do both by incorporating instructor skills in specific standards that support design fundamentals. Panning out, online course quality assessments address three main elements: organization and mechanics of the learning management environment/course design, course content (more rarely), and various teaching competencies, with these factors organized and weighed differently. Many universities and colleges use their own in-house assessments (e.g. McGahan et al., 2015). Quality assessments are usually completed by expert raters, and the relationships between assessment ratings and student success measures such as final grades, performance on summative exams, and course retention rates, are not typically investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudent Preferences and Satisfaction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome researchers have studied student preferences with implications for course satisfaction – the latter a common proxy of quality assessment. Five studies highlight the scope of student preferences for online course design and instructor practices. First, Sun et al. (2008) found that 66.1% of the variance of e-learner’s satisfaction can be explained by seven preference variables: learner computer anxiety, instructor attitude towards e-learning, course flexibility, e-learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments. Second, Gillingham and Molinari (2012) surveyed fifty-three undergraduate and graduate students in a Health Systems Management program concerning their perceptions of online courses. Students preferred the flexibility of the online modality, prompt feedback, pre-recorded video, and certain characteristics of the learning management system such as document sharing, assessment tools, and an electronic portfolio. Third, Vijay (2020) evaluated the preferences of 115 undergraduate medical students concerning their online classes. Students reported a preference for traditional teaching but appreciated the additional time online classes afforded. Video was the most preferred way of learning in online classes in comparison to audio, slides, or documents. Fourth, John et al. (2021) studied preferences for online learning components by surveying 72 Indian medical students. Sixty percent of the sample showed preference for recorded videos over synchronous sessions and the majority reported technical issues impeding learning. Fifth, Groton and Spadola (2022) investigated social work students’ preferences for online learning mechanisms using a focus group of 25 students. Seven preference themes emerged: visuals, interactive components, asynchronous structure, variety, accessibility, applied scenarios, and short/frequent assessments. In sum, two themes emerged across the student preference research – particular aspects of course design and skillful instructor behaviors. The combinative findings may be helpful for designing online courses consistent with what students perceive as helpful (or not) to their learning. For example, Placencia and Muljana (2019) made recommendations for folder organization, tabs, and LMS features based on students’ perceptions of course design that contributed to their learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnd-of-course satisfaction surveys are widely perceived as foundational to judging course quality (Bayrak et al., 2020). Yet, there isn’t much investigation into the facets of student course satisfaction. Diloreto et al (2022) created the 19-item Student Learning and Satisfaction in Online Learning Environments Instrument (SLS-OLE) based on a literature review, and a confirmatory factor analysis fit four domains: course structure/organization, learner interaction, student engagement, and instructor presence. A follow-up regression analysis found the best predictor of satisfaction and perceived learning was instructor presence. Bangert’s (2006) earlier analysis of an online course satisfaction measurement also yielded four factors: student-faculty interaction, active learning, time on task (self-regulated learning facilitated by course design), and cooperation among students. There are similarities with the SLS-OLE factors, with both models emphasizing quality relationships and course design, but the predictive power of Bangert’s factors isn’t flushed out.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDoes course satisfaction predict measures of student success such persistence, completion, and final grades? Two studies investigated the relationship between student satisfaction and continuance or completion rates. Al-Samarraie et al. (2018) found that information quality, task–technology fit, system quality, utility value, and usefulness were core factors for satisfaction with the persistent use of “e-learning” for both instructors and students. Focusing on completion, Herbert et al. (2006) studied the relationship between students’ satisfaction with institutional and instructional/course variables as assessed by the Noel-Levitz Priorities Survey for Online Learners™ (PSOL) (https://www.ruffalonl.com/enrollment-management-solutions/student-success/student-satisfaction-assessment/priorities-survey-for-online-learners/and retention). Their research demonstrated varied results across measures for completers and non-completers. However, successful completers were more satisfied with all aspects of their online course.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe correlation between satisfaction and one measure of course success - final grades - is generally positive but exhibits a range of effect sizes (Svanum \u0026amp; Aigner, 2011). Benton and Cashin (2012) reviewed student ratings research from 1970 to 2010 and reported positive but modest correlations (.10 to .30) with final grades. The investigators found similar ratings whether a course was face-to-face or online. In another study examining the relationship between satisfaction and course grades in a sample of 4290 online students, Chitkushev et al. (2014) found course satisfaction was strongly correlated with instructor satisfaction but weakly associated with final grades. In combination, the research suggests that factors other than grades, such as course design, course difficulty, and instructor characteristics, play significant roles in course satisfaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose of this Study – Use of Student Self-Reports for Survey Development\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAl-Samarraie (2017) recommended exploring the preferences of a target population before designing an online course. An easily used measure of online course satisfaction – based on direct accounts of course perceptions\u003cstrong\u003e\u0026nbsp;–\u003c/strong\u003e would add incremental value in combination with course design rubrics for comprehensive quality assessments of new and established courses. Except direct and timely student input hasn’t been factored in satisfaction survey development. One project assessed what students thought of course design assessment items. Ralston-Berg et al. (2015) invited a large sample of college students (\u003cem\u003en\u003c/em\u003e = 3,160) to rate the importance of Quality Matters standards (with items restated from a student perspective). The researchers found that, generally, students valued the QM criteria, but they did not value peer interaction in proportion to its scoring weight. Ehlers (2018) expounds that “e-learning has to be based on the learner” (p. 1) and argues that quality of a learning process is a “co-production” between the learner and learning environment. The current study applies this idea by partnering with students in crafting a satisfaction measure.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper presents the results of a three-part project to develop a student-centered satisfaction instrument. The project started with investigation that began soon after in-person general education courses at a Midwest community college had been converted to an online format in temporary response to the COVID-19 pandemic. For part one, students were asked what was helpful and what would have improved their online learning experience. Qualitative analysis of the students’ written statements prefaced the writing of an item pool based on learner preferences just after course completion. For parts two and three, the psychometric properties of the new assessment, called the Remote Learning Best Practices survey (RLBP; called “remote” with goal of usability across virtual learning modalities), was investigated using two student samples. This included its dimensionality, internal consistency reliability, and RLBP score relationships with predicted final course grades, GPA, and two summative measures of satisfaction.\u0026nbsp;\u003c/p\u003e\n\n\n\n\n\n\n\n"},{"header":"Part One Methods and Results","content":"\u003cp\u003eData was collected in asynchronous and synchronous online courses offered by the Social and Behavioral Sciences Department at an open admission, midwestern community and technical college. The institution’s enrollment varies, with 7,000 to 9,700 students pursuing over one-hundred degree and certificate pathways. Student composition is 56.8% White, 24% African American, 4% Multi-Racial, 3.4% Hispanic or Latino, 3.8% Asian, \u0026lt;1% American Indian/Alaska Native/Pacific Islander, and 7.8% Unknown. These percentages are based on an institutional student demographic five-year trend report (2018-2022).\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe qualitative data used to create the RLBP item pool was produced by 120 students enrolled in four introductory psychology and six introductory sociology online courses in the summer of 2020. Students completed an anonymous survey in the Blackboard Learning Management System about course modality preferences at the end of the semester. Neither student or instructor identifying information (including course section numbers) nor demographic data was requested to simplify data collection using the Blackboard survey tool. Instructors determined via gradebook indicators if students completed the survey to grant extra credit, with results available in batch format only. Results were sent to the primary investigator who combined them into a larger file for analysis.\u0026nbsp;\u003c/p\u003e\u003cp\u003eTwo questions concerning student perceptions were embedded in the larger survey:\u0026nbsp;\u003c/p\u003e\u003col\u003e\n \u003cli\u003e\u003cem\u003ePlease tell us about 2-3 things that were good or helpful to your learning successfully in a web class format (any format) this summer. Please write a brief explanation for each.\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ePlease tell us 2-3 things that would have improved your web learning experience this summer. Please give a brief explanation of each.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e\u003cp\u003eResponses were collated into thirty-five pages of statements. Eight-hundred and forty-four statements were produced with 502 in response to question one and 342 in response to question two. NVivo was used to identify patterns in the writing and similar themes emerged per question. For question one, five themes were identified as helpful to the learning experience: \u003cem\u003ecourse design/content\u003c/em\u003e (42% of statements), \u003cem\u003einstructor communication and interpersonal factors\u003c/em\u003e (19%), \u003cem\u003einstructor behaviors\u003c/em\u003e (16%), \u003cem\u003easpects of the learning experience that saved time\u003c/em\u003e (this theme \u003cem\u003e\u0026nbsp;\u003c/em\u003econflated with course design with exception of a few statements\u003cem\u003e\u0026nbsp;\u003c/em\u003e(12%), and \u003cem\u003elearner factors\u003c/em\u003e (behaviors or situations impacting learning; 7%). Four percent of statements in response to question one were not relevant to the course experience. For question two, four themes were identified as problematic for the learning experience: \u003cem\u003ecourse design/content\u0026nbsp;\u003c/em\u003e(48% of statements), \u003cem\u003einstructor communication and interpersonal factors\u0026nbsp;\u003c/em\u003e(20%), \u003cem\u003einstructor behaviors\u003c/em\u003e (11%), \u003cem\u003elearner factors\u003c/em\u003e (11%), and \u003cem\u003etechnology\u0026nbsp;\u003c/em\u003e(7%). Two percent of statements in response to question two did not relate to the course experience.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe final 25-item “Remote Learning Best Practices Inventory” (RLBP) was created by writing items representative of students’ commentary organized by the two largest themes: course design/content and instructor behaviors; item wording reflected the sub-themes (codes) under the main themes. “Learner factors,” such as problems with self-discipline, motivation, and time management, although an issue related to online course satisfaction, were not represented in the final item pool as the purpose was to develop a tool useful for instructors to improve course quality. \u0026nbsp;\u003c/p\u003e"},{"header":"Part Two Methods and Results","content":"\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe second project examined the relationship between RLBP total scores, a single item measure of course satisfaction, predicted final course grades, and predicted GPA. The internal consistency reliability and dimensionality of the new assessment was also investigated.\u0026nbsp;\u003c/p\u003e\u003cp\u003eData was collected from 514 participants in the last two weeks of the fall 2022 semester. Students in web introductory general education classes representing seven disciplinary areas were sent a Survey Monkey link via their class instructors. The link was provided to instructors by the primary investigator, and recipients (students/instructors) did not have access to survey responses. Students demonstrated their participation by taking a screen shot of a statement on the survey: “Proof of completion for Remote Learning Survey” and later sending it to their instructors for extra credit. Student, instructor, and class section identifying information were not included in data collection to safeguard confidentiality.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe survey included standard demographic questions, a question about the predicted grade point average (after semester’s conclusion), and a question about predicted final course grade. Respondents completed the 25 RLBP items and were asked about their overall course satisfaction via a single item measure.\u0026nbsp;\u003c/p\u003e\u003cp\u003eOut of 514 survey participants, 62 reported completing it in more than one class. One of their data sets was removed to avoid double entries. The college has a large dual credit population taking fall-spring semester courses and 87 survey respondents reported to be under 18. Once this data was removed to improve generalizability, and, after accounting for overlap between “under 18” and the multiple completer participants, 370 data entries were available for analysis.\u0026nbsp;\u003c/p\u003e\u003cp\u003eStudents surveyed included 10 criminal justice, two geography, 41 history, 78 economics, 11 political science, 128 psychology, and 100 sociology students in introductory (100-level) general education classes. Participants ranged widely in their disciplinary pathways including business (\u003cem\u003en\u003c/em\u003e = 61), education (\u003cem\u003en\u003c/em\u003e = 15), engineering (\u003cem\u003en\u003c/em\u003e = 16), food sciences (\u003cem\u003en\u003c/em\u003e = 5), health careers (\u003cem\u003en\u003c/em\u003e = 141), information technology (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 18), helping professions (\u003cem\u003en\u003c/em\u003e = 25), humanities/sciences (\u003cem\u003en\u003c/em\u003e = 58), public safety (\u003cem\u003en\u003c/em\u003e = 2), technical or skill based (\u003cem\u003en\u003c/em\u003e = 5), and other (\u003cem\u003en\u003c/em\u003e = 23), with one participant not reporting.\u0026nbsp;\u003c/p\u003e\u003cp\u003eSurvey respondents consisted of individuals who identified as female (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 270), male (\u003cem\u003en\u003c/em\u003e = 89), non-binary (\u003cem\u003en\u003c/em\u003e = 9), transgendered (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1), and other (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1). Reported ethnicities included White (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 232), Black (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 97), Hispanic/Latino (\u003cem\u003en\u003c/em\u003e = 16), Asian/Asian American (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 13), American Indian/Alaska Native (n = 2), and Other (\u003cem\u003en\u003c/em\u003e = 10). The sample age range was 18 to 24 (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 174), 25 to 34, (\u003cem\u003en\u003c/em\u003e = 122) 35 to 44 (\u003cem\u003en\u003c/em\u003e = 59), 45 to 54 (\u003cem\u003en\u003c/em\u003e = 11), and 55 to 64 (\u003cem\u003en\u003c/em\u003e = 4).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFinal grades were reported as 1 (A), 2 (B), 3 (C), 4 (D), 5 (F), and 6 (incomplete). GPA was reported as 1 (less than 2.0), 2 (2.0), 3 (2.5), 4 (3.0), 5 (3.5), and 6 (4.0). Course satisfaction was reported as 1 (\u003cem\u003every satisfied\u003c/em\u003e), 2 (\u003cem\u003esatisfied\u003c/em\u003e), 3 (\u003cem\u003eneither satisfied nor dissatisfied\u003c/em\u003e), 4 (\u003cem\u003edissatisfied\u003c/em\u003e), and 5 (\u003cem\u003every dissatisfied\u003c/em\u003e). The twenty-five RLBP items were rated 1 (\u003cem\u003estrongly agree\u003c/em\u003e), 2 (\u003cem\u003eagree)\u003c/em\u003e, 3 (\u003cem\u003eneither agree nor disagree\u003c/em\u003e), 4 (\u003cem\u003edisagree\u003c/em\u003e), and 5 (\u003cem\u003estrongly disagree\u003c/em\u003e). Items measuring final grades, course satisfaction, and the RLBP were reverse coded prior to analysis for interpretive ease.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThere were no significant differences in average scores for expected final course grades, expected GPA, course satisfaction, and RLBP scores across gender and ethnic identification categories. Because of this, analyses per disciplinary area – with some having few students - was not conducted. Most students expected a final course grade between an A and B and their GPA at semester’s end to be 3 to 3.5. Average course satisfaction was between “very satisfied” and “satisfied”, and the average RLBP score was 108.93 with a range of 36 to 125.\u0026nbsp;\u003c/p\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"532\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"6\" style=\"width: 532px;\"\u003e\n \u003cp\u003eTable I \u003cem\u003eDescriptive Statistics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003eMinimum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003eMaximum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003eMean\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cem\u003eStd. Deviation\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eFinal grade\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e.81\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eGPA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4.56\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eSatisfaction\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e.83\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRLBP total\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e108.93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e15.10\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003cp\u003eRLBP total scores were correlated with the single item measure of course satisfaction. Spearman’s rho evidenced a strong correlation (\u003cem\u003er\u003c/em\u003e = .79, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001). There was a moderate correlation between RLBP scores and predicted\u0026nbsp;final course grade (\u003cem\u003er\u003c/em\u003e = .27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) but no significant relationship between RLBP scores and predicted GPA at semester’s end (\u003cem\u003er\u003c/em\u003e = .09, \u003cem\u003ep\u003c/em\u003e = .11).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eExploratory Factor Analysis and Internal Consistency Reliability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA principal component analysis was used to investigate the dimensionality of the 25 RLBP items. Initial analysis showed that most items were not highly skewed with values in an acceptable range (.62 to 2.08; George \u0026amp; Mallery, 2010). Kurtosis values ranged from -.37 to 5.6, also in an acceptable range (Byrne, 2010). The Kaiser-Meyer-Olkin (KMO) value of .95 and Bartlett’s Test of Sphericity (χ² \u0026nbsp; 6945.18, p \u0026lt; .001) indicated the data was suitable for factor analysis. Initial factor extraction resulted in three factors with eigenvalues greater than one. The first counted for over half of the variance. As the primary intent of this study was scale development, several principal component analyses were conducted, to yield the most interpretable results.\u003c/p\u003e\u003cp\u003e\u003cimg 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AABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGghWAAAAAAtBAsAAACghWABAAAAtBAsAAAAgBaCBQAAANBCsAAAAABaCBYAAABAC8ECAAAAaCFYAAAAAC0ECwAFRFFE3W6XlpeXqd1uq9lPpSiKqN1u0/LyMtVqNVpeXqadnR212KHVbrepVqvR1taWmgVPgNFoRO12mxYWFqjf76vZM7GxsbGv0z9MZhYs9Pt9ajabScdRq9VodXWVNjc3aTQa0XA4RCcLCW4jZT4HJYoiun37NjWbTQqCQM3eNxyc1Go12tjYoCiK1CIpo9GIVldXJ5ablY2NDSIievToEXmeR0EQ0Le+9a2xgGFjY2OsLnmZsmSVh/3xpPbbw+GQbt++TW+88Qbt7e2p2U+kfe8vxJTCMBS2bQsiEo7jCN/3k7wgCITrusIwDEFEwnXd1HdhPjqdTqpeDprv+8IwDOF5Xiqd25HcTsIwFK7rihk0VVGv19WkUng+5tGOe72eICLheZ4Iw1CYpils21aLpViWNbd67nQ6gohEGIZJmud5gogy58HzvKQfyCvD5Dq3LEsEQaAWgSk9Lf02L6OuvRUxbd+x3+bRX0zVA4dhKCzLSmYyTxAEwjRN4TiOmgVzYJpmqUax33zfz+yAsoIFOW8anudNPY15BguWZQnTNJP/eeecV4+tVmsu88W4rsrg9acGGVk4oJxUDsp7mvrtWQQLQRCUbuvzNo/+YqrLEK+88grt7u6S4zjUaDTU7MTS0hK9++67NBqN1CzYZ1tbW3M9dV7UN77xDTVJ6/nnn1eTCouiiH784x+ryYfa7u4uLS8vJ/9/5StfISKiX/3qV1KpL/T7ffr444/p8uXLata+uX//vpo00YULF8gwDCIievDggZqd8vOf/5zefPNNWlxcVLNgSui3y2m1WmrSoTOP/qJysNDv95MO4+/+7u/U7DFra2tTdfhQ3nA4pB/96Edq8oFbW1ujtbU1NVmrbMOWvfXWW4cyYMpTZrBUFEV0/vx5unHjhpp16CwuLiY7J13wxoNJX3rpJTULpoR+u5xut0vb29tq8qEyr/6icrDws5/9jIiITNOkpaUlNTtTVoe/s7OTGtC0urpK3W5XLZYMxOHBTsPhMPmePAo7iiJqNpu0sLBAtVqNms1magAHd0Srq6vUbreT8vz7anlZu92m1dXVpGyj0RirKJ7+xsZGMoBra2srGXjSaDQypx8pI8sXFhbGBhYNh0Pa3NxMRt+ORiNqNBrJOpDX287ODp0+fToZ3HPq1Klk4MuTokh9bGxs0M2bN4nio2Euy+V4HXJ7WVhY0LaBeXjuuefUpFyXLl2i69evF94G8xTdDjlf/b9ou+IdVBAEYwMh2Z07d6jRaGQuU9H62tnZSfUXm5ubqfmMoijZLtXtjOJtVm5bq6urY/NbZnuU9fv9pByXzZoHindW8nxkLWsZ0/TbPA9qffNdJfyRt8FZ9Nv9fj81fXldqYNhiyrSjtrtNp07dy75X/390WiUqn/+jjw/6vyq+TK1XWxsbNBwOEyVyTK3/kK9LlEUX3ucZuCH4zjCMAzR6/WEiK8NyYNumOd5wnGc5Dc9zxOWZQnXdUW9Xk/SB4OBsG1bOI4jWq1WMkCn1Wol0+90Osn1Otd1hWVZyYenY1lW8ttCusZnWZYYDAZCxNdUTdMcu+4nT5/nxXEc4bpuUl69BsjTdxxHhGEowjBMlpfLDgYD0Wq1knnk33FdN7Vu1MFgs7hmNy+6MQuyMvXBeVwfsjAMhWEYwrKs5No4X1dXy8p5k+ZvFkzTzLwGydsKp6ltqYqi26GM21sV3F6z1rHQjLEpWl+9Xi/VL7ium+oP/ud//ie1Par1ydPkdsTX7knajqpujzzQk6ctjx/gfoo5jiMsaYAntwF5+cvi+arab/M8qHUXBEGyfnkdzarfZvwdtb54cB9ltMes/q9oO2JZ0+ZxV5wnT5/XESnbK+v1esI0zVTb4HbB0+ExO4ZhJH2czjz6i/G1WxCvDLXiipJHb8rCeCRnVh7/pprODcK27VQF8E5CXolCahhqp+TFo7nV3+BOQd3wB4NBUl7O42VTK5oHyhiGkaSJuFNQGyk3aFIaIncsaqXzRtnpdFLpWRvLYVU0WChbH3nBAm9Q6u/xNFTzDBayRjdbUhA7GAxSnZ0cYJqmObaN5KmyHQrNOiqC64My2mWv10stp6xsfXG6HATJ22NeffJ2J8srW2Z75O1fnQbXgTwN3nmoQUHRbSQPr5Oq38/bloSmr+HfVNtR1X47a975N1RZ81S1HWXJmr6Q6l9dZhH3X3I6twt1Gry8WetaNY/+ovJliGn9/d//PRERnTlzJpW+uLhIP/zhD4k01zXVQTl8Te35559PnV7h6+J516svXryYunbeaDTIcRwiInr33XeJ4tNjV65cIcuyxk7drKysUL1eJyKif/qnf0rSjx07RkREJ06coJWVlSSdvy/f9xtFEd28eZPOnz+fpFG8Hk6cOEFERP/5n/+ZSici+u53v5ukERH91V/9FRERffbZZ6n0J02V+sjzl3/5l0RE9Nd//ddq1oE7e/YseZ5HP/7xj+mZZ54hy7Lo3r17RPE6ePXVV+nWrVtJe3jllVfo/fffpzAM6e2336Zz586NnTbPMs12WNXa2hrZtk0knRZn169fpx/84AepNFa1vs6ePUsUb3/y9pjHNE2yLEtNzlRme+Q2eeHChSSN4vkTQqSuI1+7do0ajcbYAE/u6957771U+lEwq357WlXbURncHtRtJ4oi2t7eTq2L27dvk2maY+O4eBD4/fv3J156mkd/MXWwUGXnFEUR7e7uEkkbm+zrX/86UdxY5j0S98UXXySSRns/fPiQKGc+iYhOnz5NRETvv/++mlXIb37zGyIiOnfu3Ni1Lp6Hjz/+WPnW02uW9cGd9NmzZymSxrIcFo1Ggx49ekRCCOp2u8kyv/XWW/Tqq68mO77hcEj379+nixcvJoMITdOk119/XZli2kFuh9yJbm9vJ9MeDof06NGjsZ0Km1d9ffTRR/TRRx8RxfPUbDbpH//xH9VipfE0s9a1and3l27evDnWJ7zxxhtJ/jSq9NtPinm0Iw6IgyBIjV158OABXbx4MVX2448/piAIxur61KlTSRneT+jsd39ROVjgyLvKjmzSgsvR/+9///tU3n7jswLs//7v/1L/q/gWlWmj4F6vR+KLy0JjH44QYfb1EUURbW5u0smTJ+l///d/6datW2qRQ2VnZ4f+8Ic/0GuvvZakcVAp3466vLxMQRBoj0gOcjtcW1sj0zSJpCPura2tsY5UNa/66vf7tLq6Sm+88QZ985vfTM44TqPsDr7Vao31BfKnimn67SfJPNoR73z5LDXFQbJ6ZonielHrV/6oZx2KmmV/UTlYePnll4kKniJR/fmf/3ny96TRnlVX0rR4o3r22WeJiOjDDz9USqTxadWqPvjgAzUJMsyyPobDIX35y1+mjz/+mB4+fEjvvPNOodPUB2U0GtHrr79O//zP/6xm5dIFBAe9Hb799ttERHTz5k0aDod08+bNzI6Uzau+ms0mnTp1iv7hH/6B7t27R41Gg44fP64WK42Do0mne1mRs2NlTdNvPynm1Y7Onj1LpmnS/fv3qd/v087ODq2vr2eeWdrd3Z15fcy6v6gcLMgPWPnpT3+qZmfa3Nwkiq8d8obz61//Win1xUJSwQ5/1v74xz8SEdH6+jpRPO6A4nEGWR3q48ePiaZ4aBB32NxhqqL4lkr4wizr4+rVq7S3t0c3btzI3IAPm2azSe+++26pedXdVnXQ2yGf/qT48pHjONplm0d9cdDiOE4y1mFW+ADkX//1X9Usovi2OmaaJu3u7ubefsl9aVnT9NtPinm0I8YB8dtvv03Xr1/PfLYFj9d466231Cyi+IxblcuAs+4vKgcLi4uL9Itf/IKIiK5cuZLbqFmz2aQXXngh+Z9X4o9+9KOxiIqPGtXBIbP229/+Vk1KNmQ+wllaWkpOQfL1Qtkvf/lLMgxDe0Sks7S0RLZt097eHp0+fZq63W6yPvie5LJPO9RRn0Nw1MyiPqIoouFwmBy5yafY1Y1SbZsHpd1u09e+9rXMI3xuH/LT2h49ekSmaU7sKA56O+TLDnt7e6lTpVnmUV8cNKnTVfuKKr/zve99jygep6G+6XJrayv1G7xezp07R+12O5mf0WhEzWaz8pmOafttbn8ffvhhah3s7OzQo0ePkv/3A7fz//qv/0qlq+tykmnbUZk+9MyZM2QYRnLqXx2UTdJgyJs3b1Kj0UgOgqL4WSD//u//nvk9nX3pL9TbI8ry4/tB+RaPXq+Xuj2D73nOut+Ub92Q7yXm6an32Mr30sq3P4VhmNx/W6/XU7ca5d2ixbekkHRrUyi9vEa9jSSU7oV24mchiJx7WYW0XKZppuZHvrVP/o58j7L6kW+n4lts5PlmvA5s2079JqdbllXpeeDzItfVpPvIy9aHvN5brVayjnjdGIYh3Pj++Hq9ntSFEz8fg3+Tb5Oqeo96Vb7v595OyGzbTtob3wKstpE8ZbZDobnFuIowvkW4yO1hZepL7i/U9sCy6lNuK/V6Xbjxs1j4t834XQlBEFTaHnld87S43rLaPLdx9ZNVtqxp+m1eb5b0zAQvfvcKp/P6mGW/zW2Fv+O6rrBte6zO5FtlubxcP2XakZBupa3X66JeryfzJM+PWv8y3q9krUsmP7ND/hgFn7Mg26/+YupgQcQrrdPpJI2FP9yYdA3bix/Uwd/JaqDqdClu4HKjkj++72d+hxsAVx43Es63NG/hCuNggu89NwxDOI4zVpHqb/LvZs2P3EGG8X2v3PhM00xVHs+z+tGtAxF3fjzPWR3/YaDOO390O5Ci9cF4YzRNMykTSgGAvL65rLy+1HmT1/F+CuPASH2mhIrbD6+LSRu+qsh2KHLWA2Xcs16G67qFgo6i9TVpW8vbZhg/44Di9cCBgWEYSfupuj3y9NV2m9dHqm281Wrlli2rar89iB+iRHE9cDuxbTvVbtTp0pT9tpAeaMTT4m1Z3f7zfkOUaEeM24MhPUwrq/7z+qswfu7BJL34GSM8PW57Zexnf1ETVYfVHmHtdpveeOMNcl038xHUAAAA8CeVxywAAADA0wHBAgAAAGg9dcFCFEX0ySefEBHRJ598oh31CgAAAERP1ZiFfr+feoQm830/8xYTAAAAeMqCBQAAACjvqbsMAQAAAOUgWAAAAAAtBAsAAACghWABAAAAtBAsAAAAgBaCBQAAANBCsAAAAABaCBYAAABAC8ECAAAAaCFYAAAAAC0ECwAAAKCFYAEAAAC0ECwAAACAFoIFAAAA0EKwAAAAAFoIFgAAAEALwQIAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGghWAAAAACtmhBCqIk6/X6fTp06pSYDAADAEef7Pq2tranJ5YMFAAAAeLrgMgQAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGghWAAAAAAtBAsAAACghWABAAAAtBAsAAAAgBaCBQAAANBCsAAAAABaCBYAAABAC8ECAAAAaCFYAAAAAC0ECwAAAKCFYAEAAAC0ECwAAACAFoIFAAAA0EKwAAAAAFoIFgAAAEALwQIAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGjNJFiIooi2trZoeXlZzQKYqEz7abfbtLy8TLVajRYWFmhzc5OiKFKLaUVRRM1mkxYWFpLpNJvN3OmULU8Z32k0GjQajdRiMAPtdptqtdrYZzgcpsqpdVKkHvN0u11aXV1Nfmt1dZW63a5aLFG2PMwP2k9BYgphGIpOpyMsyxJEJKacHDxlyrYf27aFbdvCdV3hOI4wDEMQkajX62rRXGEYCsMwhGEYwrbt1G+bpinCMJyqPH/HNE1hWZYIgkCEYZjM72AwUIvDFLh+uE74Y9t2Zrky9Zin1WoJwzCE53lJmud5wjAM0Wq1UmVFhfIwP2g/xel754LCMExWHEBZRdqP53mpjUUIIQaDQfK9IAhSeXkcxxGu66Y2bt/3kw7DcZypyos4qFHniTsby7JSZWE6rusK13XV5DFV6jFLEASCiDJ/s9VqjdV72fIwX2g/xc3kMsTi4iKZpqkmAxRSpP0899xz1Gg0UmkrKytk2zYREX3++eepPJ3Lly/T4uJi8v/a2hrduHGDiIhu3rwplfxCmfL9fp/u379P9XqdlpaWkvTFxUVqNBq0u7s7/9OHT6goiuhf/uVf6MKFC2pWpjL1mOf3v/89ERE9++yzahYdP36cSCoj/120PMwP2k85MwkWiKjQ9WaAPJPaz8rKippEFG/wlmXl5qt441apgQgrW/7nP/85ERF99atfVbPom9/8JhER3b17V82CCt566y0KgoBOnjxJm5ub2jEhZetxkmvXrqlJ9Nlnn5FhGPTcc8+pWaXLw/5D+ylJPdVQFZ96BaiiSvvh63azGgdARKUuE2SVN01TEJHo9XqpdBGftiQiYRiGmgUl8elZ9dNqtUpdPxY59ajD16tt205+azAYCMMwMuu9bHnYf2g/5ZXrnTWqdPYArEz7GQwGwnEcQUSi0+mo2ZXorg1mySvPnY7v+6l0IQULRZcTJhsMBsLzvKT9lO248+pRhztqigM/HqSbF7SWLQ/zg/ZT3Mx6rTKdPYCqaPuRd7j8KTq4SMd13VKjmvPK8zxNChbmvaE/DfhMU5nOO68eJwmCIDUiflLQWrY8zB/aj97k3rmgop09QJay7afX66WOBopu3Fn4ToWsHXwWXXmen6w8OViY1wjmp02v1xNU8FKPrh4nGQwGol6vi3q9ntSpLmgtWx4OBtpPvuK98wRlO3sAWdX2w5cjTNNUswqr1+ulgg1ded6QszoQOViA/cNtaVJApqtHHd/3hWmayfQ9z9N24GXLw8FC+8k2s16ramcPIKZoPxzdV/muEEJ0Op1SG9yk8jwvumChyFELVNfpdARNuNQzqR7zcHtTH4Yjd+By3ZctDwcP7SfbzG6dBDgIi4uLdOLECTIMQ82aqNvt0nA4zL0tSlWk/Pr6OhERffrpp2pWksZlYH8cP36cDMPIvZ22SD3muXPnDu3t7dELL7yQSm80GtTpdIiI6Fe/+lWSXrY8HDy0n2wIFuCJUPZ+5263S3fv3i28wRct/7d/+7dERPTf//3falbyrHkuA/vjl7/8JTmOoyYTlajHPJ999pmalHjttdfUpNLl4eCh/eRQTzVUxfeXA1RRtf3wbUVZpww7nU7mqGHP84RlWZkjmMP4PQ6ysuVt2xaGYaTK8+nEMrdlQT7f98ce/y3i9pA3Or1sPWa1H76UpJYV8W8TUWq+ypaH+UD7Ka9875yBZ3qeMw5PjiLtx4hf4uK6bjLQx48H/mR9hzcyUq7p8bU+y7KEHb+YSv6QcltS2fJCCmAcxxFhGIogCJIAIiuogfJ43VuWJTzPE77vi1arlXv/edl6zGs/QhpU60rvCvB9X1iWldmply0P+w/tp7ypgwVeIeoHoAi13eS1H37zGudbliVarVbuiOUwfvOjfJTAt0VN+lQtLwuCILnNSQ4cYDYGg4GwpPvObdvODBpFxXrMaj8yPsrk7/JOJ0/Z8rC/0H7Kq4kvOmwAAACATBjgCAAAAFoIFgAAAEALwQIAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGhNFSxEUUTNZpMWFhaoVqvRwsICNZtNiqJILQqQK4oi2traouXlZTWrkNXVVarVarkfdbply8PhNMt6VPuyRqNBo9FILZZpNBrR5uZmMj8bGxtqETjE2u32WNup1Wo0HA7VorSzs5Nqd41GI7PcJNNOp9lsUq1WU5P3l/pmqaLCMExeG2zbduqNWHlv2gKQhWEoOp1Oqu2UFQTB2Nvf1E+r1apcHg6nWdYjvyHQsiwRBIEIw1A4jlPoleKu6wqK+7xOpzOxPBwuvB9T245t22rR5M23/LbHIAiEZVmF2ols2unIb8Gcp8q/5jhO6v3aIn7HNq/4eb5nG462MAwrN/5WqzXWDhnvUOQNsGx5OJxmWY+2bQsiSr3unHcilmWlysocx0n6uqz5gMPPdV3huq6aPMb3fUFEY6+F5rZmGEahNjDtdIIgEKZpVu4vp1H51/KCAc/zDmRB4GjjDaCsTqejJiU6nY4wTXMsLU9WeTicZlWP3HnX63U1KwkG1I5dzsvrB+Hw4zNKup0zq9frYwEl47aga5Ns2ulYlnX0zizoHMSCwNHGR3ezZFlW4VPRokJ5OJzK1CN30FlHl3zgowYSnU5HUM6pajg6uO5N0xStVitzB850+zRuJ0XawzTTcV03ade66eyXffk1ItKevgNQzTpYKHsqumx5OJzK1iOf0er1empWctbBMIwkTb7G7ft+qjwcHdxO1E+r1co808D5WXncTor0X1Wn4/u+sCwr+V5euf001d0QWXgE8csvv6xmAczNgwcPyDRNWllZUbMylS0Ph1PZegyCgIiIjh07pmYl9vb2kr8fPHhAe3t7ZJomPX78mBqNRuUR7XBwlpaWSAhBg8GAPM8j27aJiOjKlSvau1kePnyoJlVSZjpRFNH58+fp1q1btLi4qGbPzcyDhTt37pBpmnThwgU1C2Bubt26RfV6XU3OVbY8HE77VY8cBNy9ezeV3u12KQxDchyHtre36fTp04VvuYSDt7KyQo1Gg+7du0ee55FhGLS7u0vtdjtVznEcIiK6fv16Kp2I6PHjx0REZBiGmjWmynQuXbpEb7/9duEAeN+opxqmwafocHoOyprlZYiyp6LLlofDqUo98uncrD5LPi3M17P5EkTWIDRuw+oYBzg6ePCgfOlJxG2L654vVYRhKDzPSy5l5Y01kJWdTqfTyRxEy+1ynmZ6ZuHSpUv05ptv0trampoFMDdlT0WXLQ+H037W49LSEpF0SeIrX/mKUoLo9ddfJyKi7e1tNQuOiLNnz5Jt27S3t5c6Q7S0tES7u7tUr9fpypUr9Mwzz9ClS5dSl6++/e1vJ3/nKTOd4XBIt27dop/85CfSFA7OzIKFra0tWlhYoMuXL6tZAHNV9lR02fJwOFWpR/WUb5YiZYiITp48qSbBEcQ7688//zyVvrS0RN1ul+IbA6jb7dKxY8coCAIyDINeeumlVPk8Radz9epV2t3dpWeeeWbs6ZKM/+/3+9Iv7I+ZBAvdbpeGwyHduHFDzQKYq9FoRLu7u/Sd73xHzcpUtjwcTlXrcX19nYiIPv30UzUrSeMyRJQMhOPry7KDHHwGs3P8+HEyDKPQGarvf//7RPGOfZr6z5qOaZpk23bmh/H/X/rSl5K0faNelyjL8zxco4OpzWrMAj8+uqiy5eFwqlqPfI06qw/j+/Dl2yr5GQtZ15HD+EmkWdOCo8NxnELP6eD2MW19V5nOQYxZmOrXPM9L3fspC+PnqwMUUeQJjp1OJ3NgmcyyrIllZGXLw+FUpB7z2o9t22OP2eXB2moAEsZP/aOMp/BxIJE1WBIOF9/3M5/MORgMCj3VUd7B55XNa2+yItPJcqSCBX7alGVZwrbtsQ/ljBgGUA0Gg6TxZ23AQhmZntcZ82h4tRPPU7Y8HE5F6lHXfgaDgTAMQzjxOx6CIEgCiKw7K7i8Fb94SsT9oWEYmU+ChMOH91GWZQnP84Tv+6LVagnLsjLrXMTtTL5rQVfXuvZWZjp5eNrzVOnX5GdT6z5lIiV4OqltJm8j4CM6XdTPG3tRZcvD4VSkHie1nyAIkuf2y4FDnsFgkJSneKeT9RRIOJwGg4GwpLfd2rade6AilOBi0qOhhaa9lZ1Onrx+cj/VxBc/DAAAAJBpJndDAAAAwJMLwQIAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGhNHSy0221aXl6mWq1GCwsLtLm5SVEUqcUAckVRRFtbW7S8vKxmFRZFETWbTVpYWKBarUaNRoNGo5FaLNNoNKLNzU1aXV2lWq1GGxsbahE4IprNJtVqNTVZS207CwsL1Gw2J/Zj7XabarXa2Gc4HKpF4Qio0g9UaW+sTL+3s7OTzBf3b3NvZ+rLIsrgN0y6riscxxGGYQgq+V5ueHqFYSg6nU7qhS5V8Etb+C2AYfx69Ly3Bspc1xVEJEzTFJ1OZ2J5OLzkF9wVFcavojYMQ9i2nWqL6kuAZPw9Lssf27bVonAEVOkHqrQ3UaHfa7VawjCM5EVXQRAIy7IK9W+zpJ9LDc/zxt7SJb9quOrbtODpE4ZhoY0mD7/JTW5z3Jnr3kbI75Kf9IZBOPyCIEhe+VumHTmOI1zXTdW/7/tJIOA4Tqo8c1230quF4fCp0g9UbW+yIv0ev+pa3dfya9kNwyg8z9PKn8sJ8iIa7rjz8gGy8IZXFm9MWWezuBNQNzQ5L29nAEcLvyJ6Uueryqt/z/Nyp8VnsubVScP+qdoPVG1vqkn9Hr8GPevgm+e90+moWfsify4rsixLezQHkIWDzLJ4g8k6yuMOXw0kOp2OIJwyfmK4ritarZYQQkzdecvypsVtzjRN0Wq1MjtyOPyq9gOzbG+T+j3d9Ll/Kzv/VWXPRUWe5839Ogo8GSZtNHk4Mu/1empWctbBMIwkTb7W7Pt+qjwcPb7vC8uykqN8XedaFhGNHfjw6V/102q1cKbhCKnaD8y6vU3q93j6WW2L+zfd92dp6rshiIiGwyE1m006d+4cXb16lVZWVtQiAPsiCAIiIjp27Jialdjb20v+fvDgAe3t7ZFpmvT48WNqNBoHO8IYKouiiM6fP0+3bt2ixcVFNXsqfCfNyy+/nEpfWloiIQQNBgPyPI9s2yYioitXrhQaPQ+HQ5V+YD/b2yQPHz5Uk+ZPjR7KkqMb/pS9/gMwKcLOw20u6+hAbpt8touvAZqmmYxlCOO7Jyg+C4HTykdDvV4fG48yqyMt13ULj0vgM6qUczkMDp8q/cB+tLdJ/R7PT9alBr4MIZ853U/5c1lSr9dLFhwbDZQ1aaPJw+1tUrDAGz536lmDgnge1DEOcPh0Op3Mg5JpO28hnaLOalN5eLDbvDpumE7ZfmC/2tukfi8IgmRe+VJXGIbC87zkEmxWILEf8ueyInnwD0BRkzaaPLyxZnXsWdf0dOVnMboZ9t9gMEhdN5bNov7q9Xqlgx1uw+oRKRw+ZfqB/WxvRfq9IAiSMyEUBzG9Xi8JFrICnv0wkzELsp/85CdkGEZyLRlgPxmGoSaNKVKGiOjkyZNqEhxCV69epd3dXXrmmWfGnp7I+P9+v5/67iRbW1u0sLBAly9fVrMm+va3v01ERJ9//rmaBUeI2g/sZ3srYmlpibrdLsUH99TtdunYsWMUBAEZhkEvvfSS+pV9MfNgYXFxkU6cOFG4gwaYxvr6OhERffrpp2pWksZliCgZkPb48eMkjc170BJUY5om2bad+WH8/5e+9KXUd3W63S4Nh0O6ceOGmlXI8ePHyTAMDPA+Asr0A/vV3qbx/e9/nygOZNT53TfqqYZZsG078/oOQJ4ip+Oy8CnDrHEGfElMvq2S763Oap9h/ES1rGnB0VD1tLDneVPXu+M4yf33cLjNqh+o2t5YlX6P+7Ui8zdL5eaygMFggGctQGmTnmQm4g086/qcbdtjjz3lQWrqffJh/PQ9yri2zB1I1nVMOBp0nXde+/E8L/eaNI+QZ77vj42IF3G/V/TuCTh4s+oHqrQ3WZF+TyYHCvNua8XnUmHEL19xXTdZ2b7vp25FAShCfqdIXtuRByuqGzEHqE78bPcgCJIAIito5fL84ikh3f5WZWAbHB55nXde++HbzyzLEnb8Yjz5Q8oAMk6zLEt4nid83xetVktYlpXZ1uDwmkU/ULa9yYr0eyIe4Cjf/VB03mZtfCkL4jdh8cJaloVHn0Jp3H7Uj4qPBPKO3uQRw3LgkGcwGKRGGPOz3uFoK9N+5FHvuo/cjnhkPOfZtq3t6OFwm7YfKNPeZGoby5uOHJwe9P61Jr6YcQAAAIBMM78bAgAAAJ4sCBYAAABAC8ECAAAAaCFYAAAAAC0ECwAAAKCFYAEAAAC0ECwAAACAFoIFAAAA0EKwAAAAAFoIFgAAAEALwQIAAABoIVgAAAAALQQLAAAAoIVgAQAAALQQLAAAAIAWggUAAADQQrAAAAAAWggWAAAAQAvBAgAAAGghWAAAAAAtBAsAAACghWABAAAAtBAsAAAAgBaCBQAAANBCsAAAAABaCBYAAABAC8ECAAAAaCFYAAAAAC0ECwAAAKCFYAEAAAC0ECwAAACAVk0IIdREnX6/T6dOnVKTAQAA4IjzfZ/W1tbU5PLBAgAAADxdcBkCAAAAtBAsAAAAgBaCBQAAANBCsAAAAABa/x91AS/DhcLbbQAAAABJRU5ErkJggg==\" width=\"473\" height=\"253.231\" style=\"width: 473px; height: 253.231px;\"\u003e\u003c/p\u003e\u003cp\u003eFour criteria guided the number of factors to rotate in stage two of the analysis: the hypothesis that the RLBP was multidimensional, the screen test, the absolute and relative values of eigenvalues computed in stage one, and the interpretability of the rotated solution (Green \u0026amp; Salkind, 2014). More specifically, due to three factors meeting the eigenvalue- greater-than-one cutoff criteria in stage one (Green and Salkind, 2014), and the large disparity between the first and latter eigenvalues, two instead of three factors were rotated using a varimax procedure for the clearest solution. This produced two interpretable dimensions labeled “Helpfulness of Course Experience” (59.94% of the variance) and “Ease of Course Experience” (33.66% of the variance). All items loaded on a single factor despite two having less relative differentiation. These items are “This course has graded work that promotes learning outcomes rather than busy work” and “The course uses instructional materials that are helpful for learning” (see tables III and IV).\u003c/p\u003e\u003cp\u003eThe coefficient alpha internal reliability estimate of RLBP items was .96. The estimate for factor one was .95 and for factor two was .92.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"700\" height=\"418.133\" style=\"width: 700px; height: 418.133px;\"\u003e\u003c/p\u003e"},{"header":"Part Three Methods and Results","content":"\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe third project replicated part two procedures with addition of a summative measure of Quality Matters (QM) Higher Education Rubric, Sixth Edition Essential Standard items (labeled QM_ES). Data was collected from 135 participants at the end of the Spring 2023 semester. The 23 QM Essential Standard items were presented after the RLBP in the online survey. Six of these items were slightly re-worded (with content unaltered) for better applicability to student raters. For example, item 2.2 reads “The module/unit learning objectives or competencies describe outcomes that are measurable”. The word “weekly” was added after “unit” to reflect the many web courses at the college with weekly objectives. Another example, item 7.2 reads “Course instructions articulate or link to the institution’s accessibility policies and services”. “Or the syllabus” was added after “instructions” as the college syllabus template includes a link to these policies.\u0026nbsp;\u003c/p\u003e\u003cp\u003eData from 97 participants was available for analysis after removing double entries and dual credit students under 18. Students surveyed included those in introductory communications (\u003cem\u003en\u003c/em\u003e = 19), economics (\u003cem\u003en\u003c/em\u003e = 18), history (\u003cem\u003en\u003c/em\u003e = 24), and sociology (\u003cem\u003en\u003c/em\u003e = 34) general education classes with two participants not reporting class type. Represented disciplinary pathways included business (\u003cem\u003en\u003c/em\u003e = 17), education (\u003cem\u003en\u003c/em\u003e = 2), engineering (\u003cem\u003en\u003c/em\u003e = 4), food sciences (\u003cem\u003en\u003c/em\u003e = 1), health careers (\u003cem\u003en\u003c/em\u003e = 30), information technology (\u003cem\u003en\u003c/em\u003e = 5), helping professions (\u003cem\u003en\u003c/em\u003e = 8), humanities/sciences (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 21), public safety (\u003cem\u003en\u003c/em\u003e = 4), technical or skill based (\u003cem\u003en\u003c/em\u003e = 2), and other (\u003cem\u003en\u003c/em\u003e = 3).\u003c/p\u003e\u003cp\u003eParticipants consisted of individuals who identified as female (\u003cem\u003en\u003c/em\u003e = 69), male (\u003cem\u003en\u003c/em\u003e = 22), non-binary (\u003cem\u003en\u003c/em\u003e = 5), and transgendered (\u003cem\u003en\u003c/em\u003e = 1). Reported ethnicities included White (\u003cem\u003en\u003c/em\u003e = 64), Black (\u003cem\u003en\u003c/em\u003e = 24), and Asian/Asian American (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 9). The sample age range was 18 to 24 (\u003cem\u003en\u003c/em\u003e = 54), 25 to 34 (\u003cem\u003en\u003c/em\u003e = 27), 35 to 44 (\u003cem\u003en\u003c/em\u003e = 10), and 45 to 54 years (\u003cem\u003en\u003c/em\u003e = 6).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eExpected final course grades, expected GPA, course satisfaction, RLBP and QM_ES total score averages were computed across demographic groups with no supported reason to investigate between group differences (considering part two). Dimensionality analysis was not repeated due to the smaller sample size. The QM Essential Standard items were rated the same as RLBP items: 1 (\u003cem\u003estrongly agree\u003c/em\u003e), 2 (\u003cem\u003eagree\u003c/em\u003e), 3 (\u003cem\u003eneither agree nor disagree\u003c/em\u003e), 4 (\u003cem\u003edisagree\u003c/em\u003e), and 5 (\u003cem\u003estrongly disagree\u003c/em\u003e) and reverse scored for interpretive ease.\u003c/p\u003e\u003cp\u003eMost students expected a final course grade between A and B and their GPA at the end of the semester to be between 3 and 3.5. Average course satisfaction was between “neither satisfied nor dissatisfied” and “satisfied” and average RLBP scores = 99.58 with a range of 60 to 125.\u003c/p\u003e\u003cp\u003eThere was a strong correlation between RLBP scores and course satisfaction (\u003cem\u003er\u003c/em\u003e = .83, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). There was a moderate, positive correlation between RLBP scores and predicted final course grades (\u003cem\u003er\u003c/em\u003e = .26, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) and no significant relationship between RLBP and predicted GPAs at semester’s end (\u003cem\u003er\u003c/em\u003e = - .02, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= .85). These findings are the same size and direction as in the previous analyses.\u0026nbsp;\u003c/p\u003e\u003cp\u003eQM_ES and RLBP scores demonstrated a robust relationship (\u003cem\u003er\u003c/em\u003e = .89, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). There was also a strong relationship between QM_ES scores and course satisfaction (\u003cem\u003er\u003c/em\u003e = .79, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). QM_ES scores and predicted final course grade demonstrated a small, positive relationship (\u003cem\u003er\u003c/em\u003e = .23, \u003cem\u003ep\u003c/em\u003e = .03). There was no significant relationship between QM_ES and expected GPA (\u003cem\u003er\u003c/em\u003e = -.05, \u003cem\u003ep\u003c/em\u003e = .67).\u0026nbsp;\u003c/p\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\" class=\"fr-table-selection-hover\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd colspan=\"8\" style=\"width: 624px;\"\u003e\n \u003cp\u003e\u003cem\u003eTable V Correlations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRLBP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQM_ES\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eSatisf.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 81px;\"\u003e\n \u003cp\u003eGrade\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003eGPA\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eRLBP\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.887\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.831\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.019\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.854\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eQM_ES\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.887\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.778\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.225\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.045\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.670\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSatisf.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.831\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.778\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.315\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.014\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.225\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.315\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.573\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; -.019\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-.045\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e-.014\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.573\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.854\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.670\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e.896\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\" valign=\"top\" style=\"width: 624px;\"\u003e\n \u003cp\u003eNote. ** \u003cem\u003ep\u003c/em\u003e \u0026lt; \u0026nbsp;0.01; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. QM_ES = Quality Matters Essential Standards; Satisf. = course satisfaction; Grade = final course grade.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis project involved the creation of a novel assessment of online course satisfaction using the self-report of students\u0026rsquo; preferences. Unlike other student satisfaction measures, the users of the product drove survey creation. Our qualitative data revealed similar primary themes as identified in the e-learning preferences literature: course design and instructor behaviors. However, dimensionality analysis also suggested helpfulness and ease of learning experiences \u0026ndash; both design and instructor related - were dominant factors overlaying RLBP items. The RLBP factor composition is generally supportive of the satisfaction dimensions evidenced by DiLoreto et al. (2022) but with less emphasis on learner interaction. Twelve RLBP items (48%) tap into the instructor\u0026rsquo;s presence but just one focuses on peer interactions. This is consistent with Ralston-Berg et al.\u0026rsquo;s (2015) discovery that students valued classmate relations less than as credited by a widely used quality assessment. \u0026nbsp;It\u0026rsquo;s possible that relational instructional techniques are hard to apply in an online modality. But it is important to note that peer interactions take many forms. It\u0026rsquo;s likely that not all online learner interactions are valued equally, and that some types, such as topical engagement via guided discussions, multiple participant gamified competitions, or use of virtual study groups, are palatable or even enjoyed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInitial validation results revealed budding construct validity for the RLBP survey. Patterns of correlation followed expected directions with robust associations between overall course satisfaction, a summative measure of QM Higher Education Rubric Essential Standard items, and RLBP survey scores. Predicted final course grades and RLBP scores were moderately correlated, aligned with Benton and Cashin (2012) forty-year review of the relationship between grades and course satisfaction. The correlation between predicted overall GPA and RLBP scores was nonsignificant, hardly surprising, as students were rating satisfaction with just the single class of survey administration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for part one of the project was collected shortly after all humanities and most of the science courses at the college \u0026ndash; if not already online - were pivoted to the web modality due to COVID-19 restrictions. Thus, some study participants in project one may have been in newly transitioned classes while others enrolled in established online sections. These courses were also a mix of asynchronous and synchronous classes with synchronous a novel learning modality. Both issues may have resulted in students being more critical of their learning experiences in comparison to a more typical semester. If true, it is impossible to know if this produced heightened variance in student preferences, possibly limiting generalizability. On the other hand, this atypical situation likely magnified common problems in online courses. Our students\u0026rsquo; perceptions of problematic and helpful course experiences were similar to the general and specific online course preferences identified by previous research. But a shortcoming of the current study is our initial participants\u0026rsquo; demographic characteristics were not collected. Thus, it could not be definitively determined if student composition was similar across the three samples, although it was unlikely to be widely disparate given enrollment patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations for Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe RLBP evidenced psychometric promise in the present study by demonstrating a sensible pattern of correlations with overall course satisfaction (albeit a one-item measure), final course grades, GPA, and QM Essential Standards. An interesting follow-up would be to investigate the relationships between RLBP scores and a diverse assortment of student satisfaction and course success measures. These might include end-of-course satisfaction surveys or summative knowledge assessments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are several considerations for prospective investigation of RLBP dimensionality. First, it is possible that the RLBP survey is best conceptualized as one or even three-dimensional inventory; factor analysis decisions are partly subjective. The RLBP structural analysis should be explored with a varied number of factors and rotation types\u003cstrong\u003e,\u003c/strong\u003e both orthogonal and oblique, before deciding on a definitive solution. Second, high RLBP scores on average introduced a degree of range restriction, which may impact structure generalizability. Third, if part one of the study is replicated, dimensional exploration of fresh item pools would determine if our students\u0026rsquo; preference buckets are hardy. Fourth, replication of the item generation procedure with students enrolled in established web classes (using a particular definition, such as courses that have passed an internal quality review or those taught for several years) may flush out subtle concerns specific to developed classes. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe RLBP demonstrated excellent internal consistency reliability. In fact, its reliability is so robust, it opens to consideration whether the RLBP would be improved if select items were eliminated and replaced by others measuring evidence-based usability characteristics. As an example, design and content usability for diverse learners, including those with disabilities, are essential elements of course excellence as assessed by Quality Matters. Another example is adding an item addressing technology adequacy (such as consistent connectivity), a concern mentioned by some students in the item generation sample, but with insufficient frequency for scale representation. Adequate technology may vary enough between institutions to warrant an item when measuring online course satisfaction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite these potential limitations, it is probable that our students\u0026rsquo; perceptions of helpful and problematic learning experiences capture common strengths and pitfalls in online courses. Our students\u0026rsquo; preferences generally supported prior research evidencing quality course design and pro-student instructor behaviors/attitudes as dominant factors subsuming specific preferencesdriving satisfaction. More granularly, our students preferred frequent assessments (\u0026ldquo;graded work\u0026rdquo;), videos, interesting learning activities, and student-centered instructor interactions. RLBP items are actionable, and the scale can be used for a mid- or end-of-semester check of student satisfaction. Nonetheless, whether the RLBP is a valid and pragmatic assessment of student satisfaction, based on reliable preferences for students at large, is a topic of future investigation. It is hoped that a satisfaction survey like the RLBP, based on student voices in real time, will be routinely incorporated into a multiple measure approach of online course quality assessment.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding declaration: no funding was received for this project and the author has no financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical Trial Number: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003eConsent to Participate declaration: not applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\n\u003cp\u003eUniversity of Cincinnati Institutional Review Board, protocol: 2020-0538\u003c/p\u003e\n\u003cp\u003eThe IRB classified the research as “Non-Human Research Determination” which means exempt. The IRB reviewed the study submission and determined that the proposed activity is not research involving human subjects as defined by DHHS, DOJ and FDA regulations.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis paper was written by a single author.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability Declaration The data that supports the findings of this study are available upon reasonable request from the author. Please contact the author at heather.hatchett@cincinnatistate to request the data. When doing so, please provide your institutional affiliation and purpose for request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBangert, A. W. (2006). 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CHLOE 9: Strategy Shift: Institutions respond to sustained online demand, the changing landscape of online education, 2024. Retrieved from the Quality Matters website: https://qualitymatters.org/qa-resources/resource-center/articles- resources/CHLOE-9-report-2024\u003c/li\u003e\n\u003cli\u003eSmith, T. C. (2005). Fifty-one competencies for online instruction. \u003cem\u003eThe Journal of Educators Online\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 1-18. http://www.savie.qc.ca/CampusVirtuel/Upload/Fichiers/fifty%20one%20competencies.p df\u003c/li\u003e\n\u003cli\u003eSvanum, S., \u0026amp; Aigner, C. (2011). The influences of course effort, mastery and performance goals, grade expectancies, and earned course grades on student ratings of course satisfaction. \u003cem\u003eBritish Journal of Educational Psychology\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(4), 667\u0026ndash; 679. https://doi.org/10.1111/j.2044-8279.2010.02011.x\u003c/li\u003e\n\u003cli\u003eSun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., \u0026amp; Yeh, D. (2008). What drives successful e- Learning? An empirical investigation of the critical factors influencing learner satisfaction. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(4), 1183\u0026ndash;1202. https://doi.org/10.1016/j.compedu.2006.11.007\u003c/li\u003e\n\u003cli\u003eVijay, R. (2020). Comparative evaluation of COVID-19 pandemic enforced online teaching versus traditional teaching from point of view of medical students. \u003cem\u003eInternational Journal of Basic \u0026amp; Clinical Pharmacology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 36-43. https://doi.org/10.18203/2319- 2003.ijbcp20205535\u003c/li\u003e\n\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":"online course satisfaction survey, online course satisfaction","lastPublishedDoi":"10.21203/rs.3.rs-6414745/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6414745/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper describes the development of a new measure of student online course satisfaction called the Remote Learning Best Practices (RLBP) survey. \u0026ldquo;Remote\u0026rdquo; learning encompasses asynchronous online courses and those with a synchronous virtual component such as lectures via Zoom or Microsoft Teams. Unlike other instruments of its kind, RLBP creation was guided by a qualitative analysis of students\u0026rsquo; reports of their course experiences. This unique methodology is to address that direct student input has not been a primary consideration in satisfaction survey development. Rational refinement of the initial item pool resulted in a 25-item scale with higher scores indicating stronger course satisfaction. RLBP factor composition favors the prominence of instructor presence and course design but downplays student interaction. Investigation of scale psychometric properties demonstrated a clear factor structure, strong reliability, and emerging evidence of construct validity. 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