Examining the Connections Between Time, Length, and Specificity Factors in Homework and Undergraduate Grade Outcomes

This study was conducted with students previously enrolled in a 200-level educational psychology course from the Fall 2018 and Fall 2019 semesters (N = 331, students per section ranged from 23 – 31). The purpose of this study was to examine the relationships between time, length, and specificity factors found in homework taken from Canvas on students’ homework, exam, and final course grades. The time and length factors taken from a graded homework assignments were used to examine homework scores while the mean values and standard deviation values for these factors taken from all of the homework assignments in a unit were used to examine exam scores. The standard deviation values were used as a measure of consistency among all submitted homework assignments. The specificity factor was created from all of the graded homework assignments and used to examine the final scores in the course. Several linear mixed models were used to individually examine the relationships between the time and length factors on students’ graded homework scores and unit exam scores. A linear regression was used to examine the relationship between the specificity factor and students’ final scores in the course. Homework scores were significantly related to exam scores. The results among the time factors yielded some significant relationships, but the significant relationships were not meaningful. The results for the length factors, however, were significant and meaningful. The results for the specificity factor were not significant. Among these factors examined, length factors appear to be the strongest contributor to students’ grades

COLLEGE STUDENTS’ SELF-REGULATION IN ASYNCHRONOUS ONLINE COURSES DURING COVID-19: A CONVERGENT MIXED METHODS APPROACH

The purpose of this dissertation study was to use a convergent mixed methods approach to understand college students’ self-regulation in asynchronous online courses in Fall 2020. Since the start of the COVID-19 pandemic, asynchronous online modalities have been more broadly utilized in higher education. Although undergraduate students can have greater flexibility in how they engage with their courses, students may regulate their learning differently when facing a web-based instructional modality, which may affect their academic performance. According to Bandura’s social cognitive theory, students’ beliefs in their self-regulatory capabilities are interdependent with self-regulatory behaviors. In particular, academic procrastination has been often observed in college students even though they are expected to be more self-regulated and independent learners. Rarely have researchers sought to examine the bidirectional relationship between self-efficacy for self-regulated learning and procrastination behaviors and its impacts on course performance. Little is also known about students’ perceived challenges in asynchronous online courses in conjunction with their levels of self-efficacy for self-regulated learning and procrastination behaviors. The following research questions guided the investigation of this dissertation: (1) What is the relationship between students’ self-efficacy for self-regulated learning, academic procrastination, and course performance? (2) What do students report as the most challenging aspect(s) of their asynchronous online courses? and (3) What are the major challenges experienced by students with low and high levels of self-efficacy for self-regulated learning and academic procrastination? Undergraduate students (N = 1,216; 74.7% White, 69.3% female) attending a public U.S. university were surveyed at two time points (Time 1: September, Time 2: November) in Fall 2020. Students were enrolled in 1 of 35 participating course sections taught in an online, fully asynchronous modality. Students’ self-efficacy for self-regulated learning and academic procrastination were assessed via self-report rating scales. Students’ self-rated performance and their final course grades were outcomes of interest. An open-ended question prompted students to describe the biggest challenge(s) they had experienced in their asynchronous online courses. A cross-lagged panel model revealed that students with higher self-efficacy for self-regulated learning at Time 1 tended to have lower academic procrastination at Time 2, which resulted in more desirable course performance. However, students who reported high academic procrastination at Time 1 tended to have lower self-efficacy for self-regulated learning at Time 2, which resulted in less desirable course performance. Inductive coding of students’ open-ended responses revealed that time management was perceived as the most challenging aspect of asynchronous online learning at both time points. Students with higher self-efficacy for self-regulated learning and those with lower academic procrastination were more likely to indicate that they did not experience any challenges. The findings highlight the ways in which students’ beliefs in their self-regulatory capabilities and procrastination behaviors are related to each other and differently contribute to course performance. This study has theoretical and practical implications for timely support of college students’ self-regulation in asynchronous online learning courses during and after COVID-19

A Prediction-Based Framework to Reduce Procrastination in Adaptive Learning Systems

Procrastination and other types of dilatory behaviour are common in online learning, especially in higher education. While procrastination is associated with worse performance and discomfort, positive forms of delay can be used as a deliberate strategy without any such consequences. Although dilatory behaviour has received attention in research, it has to my knowledge never been included as an integral part of an adaptive learning system. Differentiating between different types of delay within such a system would allow for tailored interventions to be provided in the future without alienating students who use delay as a successful strategy. In this thesis, I present four studies that provide the basis for such an endeavour. I first discuss the results of two studies that focussed on the prediction of the extent of dilatory behaviour in online assignments. The results of both studies revealed an advantage of objective predictors based on log data over subjective variables based on questionnaires. The predictive performance slightly improved when both sets of predictors were combined. In one of these studies, we implemented Bayesian multilevel models while the other aimed at comparing various machine learning algorithms to determine the best candidates for a future inclusion in real-time predictive models. The results reveal that the most suitable algorithm depended on the type of predictor, implying that multiple models should be implemented in the field, rather than selecting just one. I then present a framework for an adaptive learning system based on the other two studies, where I highlight how dilatory behaviour can be incorporated into such a system, in light of the previously discussed results. I conclude this thesis by providing an outlook into the necessary next steps before an adaptive learning system focussing on delay can be established

Predicting underperformance from students in upper level engineering courses

Recent research in academic analytics has focused on predicting student performance within, and sometimes across courses for the purpose of informing early interventions. While such an endeavor has obvious merit, modern contructivist learning theory expresses an importance on more individualized support for students. In keeping with this theory, this research describes the development of a model that predicts student performance within a course, relative to their past academic performance. This study is done using the minimum sources of data possible while still developing an accurate model. Useful logistic models using data from the institution’s student information system, learning management system, and grade books some useful findings are developed. While each source of data was able to predict student success independently, the most accurate model contained data from both the grade book and student information system. These models were able to accurately identify students on track to underperform relative to their own cumulative grade point averages within the first seven weeks of a course, aligning with the studied institution’s existing requirements for a manual early intervention system

How Engaged are our Students? Using Analytics to Identify Students-at-Risk

Learning Management System (LMS) analytics have become an area of increasing interest and development. The potential to better understand our students’ levels of engagement provided by the systems have, to date, has been underutilized information resources. The study reported here looks at the relationship of student and staff engagement in the LMS and considers the levels of predictability in student behavior leading to failure. Also considered is the impact of the lecturer on the student engagement of poor and high performing students

Time Is On My Side . . . Or Is It?: Time of Day and Achievement in Asynchronous Learning Environments

Previous research suggests that the optimal time of day (TOD) for cognitive function for young adults occurs in the afternoon and evening times (Allen, et al. 2008; May, et al. 1993). The implication is college students may be more successful if they schedule classes and tests in the afternoon and evening times, but in asynchronous learning environments, “class” and tests take place at any TOD (or night) a student might choose. The problem is that there may be a disadvantage for students choosing to take tests at certain TOD. As educators, we need to be aware of potential barriers to student success and be prepared to offer guidance to students. This research study found a significant negative correlation between TOD and assessment scores on tests taken between 16:01 and 22:00 hours as measured in military time. While this study shows that academic performance on asynchronous assessments was high at 16:00 hours, student performance diminished significantly by 22:00 hours. When efforts were taken to mitigate the extraneous variables related to test complexity and individual academic achievement, the effect TOD had on assessment achievement during this time period was comparable to the effect of test complexity on that achievement. However, when analyzed using a small sub-set of the data neither GPA nor TOD could be used to predict student scores on tests taken between 16:01 and 22:00 hours. Finally, individual circadian arousal types (evening, morning and neutral) (Horne & Ostberg, 1976) and actual TOD students took tests were analyzed to determine if synchrony, the match between circadian arousal type and peak cognitive performance, existed. The synchrony effect could not be confirmed among morning type students taking this asynchronous online course, but evidence suggests that synchrony could have contributed to student success for evening types taking this asynchronous online courses. The implication of this study is that online instructors, instructional designers and students should consider TOD as a factor affecting achievement in asynchronous online courses. Results of this research are intended to propose further research into TOD effects in asynchronous online settings, and to offer guidance to online students as well as online instructors and instructional designers faced with setting deadlines and advising students on how to be successful when learning online

Predicting Procrastination: The Role of Academic Achievement, Self-efficacy and Perfectionism

The aim of this study was to examine the relations of academic achievement, self-efficacy, and perfectionism with procrastination in University students, and to examine whether procrastination can be predicted by academic achievement, self-efficacy, and perfectionism dimensions. 227 University students from different faculties completed Tuckmans' procrastination scale, Almost Perfect Scale – Revised (APS-R; Slaney Rice, Mobley, Trippi, & Ashby, 2001) and General self-Efficacy Scale (GSE; Schwarzer & Jerusalem, 1995), as well as data about academic achievement at the end of last academic year. Results have shown negative correlations of academic achievement, self-efficacy and adaptive perfectionism with procrastination, and a positive correlation between maladaptive perfectionism and procrastination. Results have also shown that self-efficacy is positively correlated with adaptive perfectionism and negatively with maladaptive perfectionism. Maladaptive perfectionism was a positive predictor of procrastination, while academic achievement, self-efficacy and adaptive perfectionism were all negative predictors. Finally, we used Hayes bootstrapping method to examine possible mediations. The results have shown that self-efficacy, by its self, is not a significant mediator, while paths containing self-efficacy and adaptive or maladaptive perfectionism mediate the relation between academic achievement and procrastination. Furthermore, both adaptive and maladaptive perfectionism mediated the relation between self-efficacy and procrastination

The Dynamic Effects of Subconscious Goal Pursuit on Resource Allocation, Task Performance, and Goal Abandonment

We test two potential boundary conditions for the effects of subconscious goals—the nature of the goal that is activated (achievement vs. underachievement) and conscious goal striving. Subconscious achievement goals increase the amount of time devoted to skill acquisition, and this increase in resource allocation leads to higher performance when conscious goals are neutral. However, specific conscious goals undermine the performance benefits of subconscious achievement goals. Subconscious underachievement goals cause individuals to abandon goal pursuit and this effect is mediated by task performance. Difficult conscious goals neutralize the detrimental effects of subconscious underachievement goals but only if implemented before performance is undermined. Overall, these results suggest that subconscious achievement goals facilitate task performance, subconscious underachievement goals trigger goal abandonment, and difficult conscious goals moderate these effects depending on the level of resource allocation and timing of goal implementation

The Relationship between Academic Procrastination and Academic Performance of Freshmen Students from a Teacher Education Institution

The academic environment is full of challenges and obstacles. With this idea, students promote some unconventional practices in studying. One of which is academic procrastination. This study analyzed the relationship between academic procrastination and academic performance of freshmen students from a teacher education institution. The researcher used a descriptive-correlational research design for this study. Ninety (90) freshmen students took part in the survey using a convenience sampling technique in the academic year of 2018-2019. This study used an adapted instrument for data gathering through a survey. The study also utilized SPSS 20 to analyze the data. Results showed that the respondents procrastinate in their academic activities. In terms of academic performance, professional education subjects got the lowest rating score and the major subjects got the highest. In addition, the program, scholarship status, and religion of the respondents got significant statistical differences. Furthermore, the study also obtained a low indirect relationship between academic procrastination, general education subjects, and professional education subjects. From the findings of the study, the researcher provided pertinent recommendations for parents, students, instructors, and the institution

The relationships between procrastination and motivational aspects of self-regulation

Many students in Malaysia are affected by procrastination. This study examines the relationship between academic procrastination and the motivational aspects of self-regulation. A sample, consisting of 310 undergraduates from two Universities in Perak, Malaysia, was recruited to complete a modified version of the Procrastination Assessment Scale for Students (PASS) and the Academic Motivation Scale – College (AMS-C 28). Interviews and focus groups were conducted to obtain details of social environments that contributed to students’ procrastination in the engagement of academic activities. Results indicated that there was a significant negative correlation between academic procrastination and the intrinsic motivation. A significant positive correlation was found between academic procrastination and extrinsic motivation. The identified motivation style under the extrinsic categories was found to be most frequently used. The findings from qualitative data analysis gave explanations for the quantitative findings. Implication were discussed in the context of Malaysia where students always internalised parents’ and society’s expectations in their academic careers