Time Management Factors for Success in Higher Education (PDF)

Time Management Factors for Success in Higher Education Citation Bisbee, Dorothy. 2019. Time Management Factors for Success in Higher Education. Master's thesis, Harvard Extension School. Permanent link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37365102 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA Share Your Story The Harvard community has made this article openly available. Please share how this access benefits you. Submit a story . Accessibility  Dorothy Bisbee A Thesis in the Field of Psychology for the Degree of Master of Liberal Arts in Extension Studies Harvard University May 2019 Time Management Factors in Higher Education Success: An Exploration of Self-Report and Learning Management System Predictors in Continuing- and First-Generation Students  Abstract For this exploratory study, 109 adult students (73.4% female) completed an online survey with measures of time management behavior and college wellbeing during the fall semester. Students were in 10 courses at a continuing education school within a large northeastern U.S. university. On a follow-up survey, 87 reported their grades and answered additional questions about time use and management. Factor analysis of self- report measures identified a three-factor structure for time management: Satisfaction with Time Use, Monitoring and Evaluating, and Planning and Prioritizing. Satisfaction with Time Use best predicted college wellbeing on the College Student Subjective Wellbeing Scale (CSSWQ, Renshaw & Boligno, 2016). Number of time management tools used negatively predicted grades and course completion, and the Mechanics dimension of the Time Management Behavior Scale (Macan, Shahani, Dipboye & Phillips, 1990) positively predicted grades and course completion. Each student’s activity on the course learning management system (LMS) was collected, de-identified, and used to show study times of day. Study times of day did not emerge as significant predictors. Some differences between first-and second-generation college students were seen: first-gen students worked more hours per week, on average, than their peers, and fewer of them got at least seven hours of sleep per night. Still, their grades and course completion rates were similar to their peers’. Satisfaction with Time Use was a better predictor of grades and course completion than Mechanics for first-generation students. Directions for future research are identified.  iii Dedication To Yogi and Phoenix, for your patience and for who you are.  iv Acknowledgments Jenny Gutbezahl went beyond the call as my Research Director. Her humor, patience, wisdom, empathy, teaching skills, incisive editorial suggestions, and ability to be real were fundamental to this thesis. I also especially want to acknowledge Shelley Carson, the catalyst for my research work. Back in 2013, before I was her student or even enrolled at Harvard Extension, Dr. Carson offered to help with my first research study. She did the lion’s share of design and analysis in that study, teaching me along the way, and has been a role model, mentor and inspiration in and out of the classroom. For the current study, Andy Engelward, Principal Investigator, volunteered many hours of logistics and brainstorming; his optimism, kindness and problem solving revived the study at times when it seemed all was lost. Dante Spetter, my Research Advisor, guided me through the thesis proposal process from early on, and I am grateful for her clarity, experience and careful format review of this thesis. Glenn Lopez of Harvard’s Office of the Vice Provost for Advances in Learning (VPAL), collected, de-identified and prepped an entire semester of data for this study. I had no idea how much I was asking him when I requested his help. Ilia Rushkin, also at VPAL, for shared ideas and support. Thanks to Helen Consiglio and Leslie Gindro at Regis College for their generous advice, and to Chuck Houston at Harvard Extension for listening and advising.  v Table of Contents Dedication.......................................................................................................................... iii Acknowledgments.............................................................................................................. iv List of Tables ................................................................................................................... viii List of Figures......................................................................................................................x Chapter I Introduction..........................................................................................................1 Definition of Terms..................................................................................................2 Background..............................................................................................................2 Time Management in Higher Education......................................................3 Changing Demographics..............................................................................4 Mental Health and Wellbeing......................................................................6 Technology ................................................................................................10 Learning Analytics.....................................................................................11 Research Review....................................................................................................12 Defining and Measuring Time Management with Self-Report Data.........12 Identifying Patterns of Actual Time Use with Learning Analytics ...........17 Predicting Grades, Wellbeing, and Course Persistence.............................18 Significance of the Study.......................................................................................26 Study Purpose and Research Questions.................................................................27 Chapter II Method and Materials...................................................................................29 Participants.............................................................................................................29 Measures ................................................................................................................33 Survey Instruments ....................................................................................33  vi Data Analysis.........................................................................................................35 Factor Analysis ..........................................................................................37 Grade and College Well-Being: Linear Regression...................................37 Chapter III Results.............................................................................................................39 Outcome Variables.................................................................................................39 Grades ........................................................................................................39 Course Persistence .....................................................................................40 Factor Analysis ......................................................................................................41 Predictors ...............................................................................................................42 Overall Findings.....................................................................................................42 Analysis..................................................................................................................43 Study Times of Day ...............................................................................................47 Group Differences..................................................................................................50 Chapter IV Discussion...................................................................................................55 Limitations and Future Directions .........................................................................62 References..........................................................................................................................67 Appendix A. Web Site Survey of College Time Management Resources .....................78 Appendix B. Participant, Subject and Requirement Breakdowns for Courses...............80 Appendix C. Self-Report Survey Measures....................................................................82 Appendix D. Comparison of Change Scores on SSTS, 2016 Study...............................87 Appendix E. Data Analysis Procedures..........................................................................88 Survey Data Processing .............................................................................88 LMS Data Processing ................................................................................89  vii Survey Reliability Analysis .......................................................................92 Factor Analysis ..........................................................................................94 Analysis of Distribution Across Groups....................................................97 Appendix F. Demographic Differences Between First- and Continuing-Gen Students101  viii List of Tables Table 1 Hypothetical Correspondence Between Dimensions of Time Management....14 Table 2 Survey Measures With Chronbach’s alpha ......................................................36 Table 3 Outcome Measure Descriptive Data.................................................................41 Table 4 Predictor Variable Descriptive Data................................................................43 Table 5 Best Regression Model for Predictors of College Wellbeing (n = 87).............45 Table 6 Best Model for Predictors of Grade and Course Completion (n = 87)............47 Table 7 First-Gen: Best Model for Predictors of College Wellbeing (n = 40) ............51 Table 8 First-Gen: Best Model Predicting Grades & Course Completion (n = 31)....51 Table A1 Results of Google Search for College Time Management Offerings............78 Table A2 Time Management Offerings Mentioned on 10 College Web Sites ...............79 Table B1 Participants and Course Topics (n = 109) ....................................................80 Table B2 January Survey Participants (n = 87), with Course Topics ..........................80 Table B3 Sample Course Requirements .......................................................................81 Table D1 Pre/Post Change Scores on SSTS by Group with ANOVA, 2016 Study ........87 Table D2 Follow-Up Change Scores on SSTS by Group with ANOVA, 2016 Study.87 Table E1 Variables in Time Factors, in Order of Loading with Source Measures.......96 Table E2 Group Differences in Variables: Summary of Significant p-values from Non- parametric Rank-order Tests (Mann-Whitney and Kruskal-Wallace), Before Bonferroni correction 98  ix Table E3 Pearson Correlations Between Study Times, Sleep, Number of Tools, and Track & Monitor, and Demographic Factors with p-values < .05 Before Bonferroni Correction 100 Table F1 Key Differences Between First- and Continuing-Gen Study Participants ...101 Table F2 Differences (p < 0.10)a in Mean Age, Hours of Work, and Study Times, Between First-Gen and Continuing-Gen Study Participants...........................................102  x List of Figures Figure 1. Participant Flow..............................................................................................31 Figure 2. Final Grade Distribution by Count (n = 80)....................................................40 Figure 3. Combined Daily Student Patterns of Course Access......................................48 Figure 4. Percentage of Student Access to Course Web Site.........................................49 Figure 5. Agreement with “Other students here like me as I am” vs. Grade .................53 Figure 6. Course Background vs. Final Grade, Based on First-Gen status....................54 Figure E1. Sample Course Patterns................................................................................91 Figure F1. Bar Chart Comparison of Hours Worked for First- and Continuing-Gen..103 Figure F2. Bar Chart Comparison of Ages of First- and Continuing-Gen...................104  1 Chapter I Introduction This thesis is an exploration of the relationship between time management behavior and adult student success, and a look at whether time management measures need updating for today’s online world. Participants were 109 students taking online or hybrid courses at a large continuing education school associated with a university in the U.S. northeast region. The study built on past time management research by including predictors that come from today’s online world, and exploring differences between first- and continuing-generation students. Success was measured by final grades, sense of wellbeing, and course completion. Predictors came from self-report, using old and new questions, and data from Canvas extracted throughout the semester. This was an exploratory study, with no specific a priori hypotheses as to which predictors would show the strongest association, or whether they would differ for first- generation students. Factor analysis, multiple linear regression, bivariate correlation and other techniques were used to explore the relationships between time management predictors, and academic success. Because the sample size was smaller than expected, and the variance in grades and course outcomes was minimal, there was insufficient power to detect smaller effects. However, some significant findings about predictors of college wellbeing, grades and course completion emerged, and areas for future research were identified.  2 Definition of Terms College wellbeing: academic satisfaction, school connectedness, self-efficacy, and college gratitude, as measured by the College Student Subjective Wellbeing Scale (Renshaw & Bolognino, 2014; Renshaw, 2016) First-generation: having no parent or guardian who has attended college Learning analytics: study of large-scale data to describe student behavior and improve education Learning Management System (LMS): an online platform that links students to course resources and logs student activity Procrastination: delaying action despite knowing the delay will sabotage goals Time management: the use of habits, strategies, and deliberate behaviors for optimal allocation of time to achieve goals or preferences Background The best self-report time management measures date to the early 1990’s, before education went online, so the findings may not generalize to today’s students. Also, the academic support strategies offered to students are often not evidence-based (McCabe, 2018). The growing field of learning analytics may offer some help. Researchers are exploring how to use LMS log data to inform students, instructors, and academic support staff. While this is promising, most of the studies have used aggregated data, and few  3 have combined individual student data with log data, so it is unclear how learning analytics can best complement or supplant surveys. This study explores how self-report and LMS data may complement one another in identifying time management predictors of academic success. The background discusses past research into time management, how it relates to higher education in the 21st century, and what old and new instruments are available to measure it. Popular and well-validated time management measures like Macan’s Time Management Behavior Scale are discussed, along with recent research in the growing field of learning analytics. Both methods’ predictive value for student success is reviewed. Time Management in Higher Education It is easy to find advice on time management. In April 2016, a Google search for the words “time management for students” (no quote marks were used) yielded 57.9 million hits returned in half a second. An identical search in March 2019 yielded 1.67 billion hits in less than half a second. It is also easy to find time management tools. Everything is available on students’ phones and laptops: the time of day, the date, calendars, to-do lists, planners, project management apps, apps to track time on the Internet, apps to block access to certain sites at chosen times of day, apps to focus, apps to track sleep, and so on. With all these resources, it can be difficult to disconnect from the Internet. Researching, evaluating and implementing time management advice can be a form of procrastination in itself. Most colleges and universities offer resources to assist students with time management. The results of a November 2017 Google search for “college academic support services” followed by search for “time management” in the first ten colleges that  4 appeared, showed all but one college offering time management advice and/or support in the form of workshops or tip sheets (Appendix A). Several colleges offered time management as a full day’s topic in a first-year seminar, and one even offered a certificate in productivity and time management. McCabe (2018) surveyed academic support centers at 77 U.S. colleges. When she asked center directors for their top three strategy recommendations, 58% of the responses related to time management (McCabe, 2018). Based on a study of 83 freshmen on academic warning or probation at a U.S. college, Balduf (2009) recommended that orientations for all college freshmen include time management strategies. Students are initially referred for academic support when instructors, or the students themselves, report that they are struggling. Academic coaches, learning specialists, tutors, advisors and others help students with academic skills one-on-one, in groups, and by offering information on academic support web pages. While tutors focus on subject-specific guidance, academic coaches and learning specialists offer more general assistance. They may help students with time management and general organization skills, offer strategies for better reading, writing, studying, and test taking, and encourage students to improve sleep and exercise habits, manage stress and seek better life balance. In the end, everything relates back to time management. Changing Demographics The demographic profile of higher education institutions has changed a lot since the start of the new Millennium. Student bodies are more diverse in terms of race/ethnicity and age. For example, nearly 70% U.S. postsecondary students were White in the year 2000. By 2016, that figure had dropped below 57% (U.S. Department