Year two: Effect of procrastination on academic performance of undergraduate online students

Procrastination presents problems not only for undergraduate students, but also for undergraduate faculty, and the effects of student procrastination on academic performance is a joint concern. This two-year follow up study seeks to better understand the relationship between academic performance and the actual time of submission of assignments relative to the deadline imposed on those submissions. The authors investigated the effect of academic assignment submission time and the academic grades earned before, on, and after the assignment submission deadline. These results continue to suggest that the earlier assignments are submitted, the higher the grades tend to be. Therefore, online faculty need to encourage undergraduate online students to develop a better understanding of the potential benefits of adopting the habit of earlier submission of assignments

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

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

The Relation between Academic Procrastination of University Students and Their Assignment and Exam Performances: The Situation in Distance and Face-to-Face Learning Environments

The relation between assignment and exam performances of the university students and their academic procrastination behaviors in distance and face-to-face learning environments was investigated in this study. Empirical research carried out both in face-to-face and online environments have generally shown a negative correlation between academic procrastination and academic performance. However, the effect of academic procrastination on assignments in distance learning setting has not been analyzed extensively. To understand the interaction between academic procrastination and the learning environment; assignment and exam performances of eighty-eight university students in face-to-face (FtF) and distance learning (DL) environments were investigated. According to the findings of the study, students’ academic procrastination and assignment scores were negatively correlated in both environments but especially in DL setting. Contrary to this, academic procrastination and exam scores were correlated to each other only in FtF environment. On the other hand, there was no correlation between total assignment and exam scores for DL group, while a medium positive correlation was found in FtF group. The findings of binary logical regression analysis demonstrated that predictive value of the DL environment for assignment score is much stronger than academic procrastination behavior of students

Bagaimana prokrastinasi akademik mahasiswa indonesia pada masa pandemi covid-19?: pengujian deskriptif dan komparatif

Previous research reported that online learning can be a cause of academic procrastination behavior in students, for that research on student academic procrastination during the Covid-19 pandemic is important. The purpose of this study was to determine how students' academic procrastination was and to find out the differences in student academic procrastination in terms of gender and university status. A total of 326 students who were taken using the convenience sampling technique participated in this study. The research data was taken using the Irrational Procrastination Scale (α=0.764) which was adapted by the researcher into Indonesian. The results of the descriptive analysis showed that 82.51% of the research participants had a level of academic procrastination in the moderate to high category. The comparative test did not find any significant difference in academic procrastination in terms of gender and university status. Research implications will be discussed. Penelitian sebelum ini melaporkan pembelajaran daring dapat menjadi penyebab perilaku prokrastinasi akademik pada mahasiswa, untuk itu penelitian prokrastinasi akademik mahasiswa pada masa pandemi Covid-19 menjadi penting dilakukan. Tujuan penelitian ini adalah untuk mengetahui bagaimana prokrastinasi akademik mahasiswa dan untuk mengetahui perbedaan prokrastinasi akademik mahasiswa ditinjau dari jenis kelamin serta status Universitas. Sebanyak 326 mahasiswa yang diambil menggunakan teknik conveniance sampling ikut berpartisipasi dalam penelitian ini. Data penelitian diambil menggunakan Irrational Procrastination Scale (α=0,764) yang telah diadaptasi oleh peneliti ke dalam bahasa Indonesia. Hasil analisis deskriptif menujukkan sebanyak 82,51% partisipan penelitian memiliki tingkat prokrastinasi akademik dalam kategori sedang sampai tinggi. Uji komparatif tidak menemukan adanya perbedaan yang signifikan prokrastinasi akademik ditinjau dari jenis kelamin dan status Universitas. Implikasi penelitian akan dibahas. &nbsp

Due Tomorrow, Do Tomorrow: Measuring and Reducing Procrastination Behavior Among Introductory Physics Students in an Online Environment

This work is focused on the measurement and prevention of procrastination behavior among college level introductory physics students completing online assignments in the form of mastery-based online learning modules. The research is conducted in two studies. The first study evaluates the effectiveness of offering students the opportunity to earn a small amount of extra credit for completing portions of their homework early. Unsupervised machine learning is used to identify an optimum cutoff duration which differentiates taking a short break during a continuous study session from a long break between two different study sessions. Using this cutoff, the study shows that the extra credit encouraged students to complete assignments earlier. The second study examines the impact of adding a planning-prompt survey prior to a string of assignments. In the survey, students were asked to write a plan for when and where they would work on their online homework assignments. Using a difference in differences method, a multilinear modeling technique adopted from economics research, the study shows that the survey led to students completing their homework on average 18 hours earlier and spreading their efforts on the homework over time significantly more. On the other hand, behaviors associated with disengagement, such as guessing or answer-copying, were not impacted by the introduction of the planning prompt. These studies showcase novel methods for measurement of procrastination behavior, as well as evaluating the effectiveness of the designed interventions to help students avoid waiting until the last minute to make progress on assigned tasks

Combinative Class Management to Reduce Student Academic Procrastination during the Covid-19 Pandemic

The prevalence of academic procrastination among students in various cultures around the world is up to 70%. The Covid-19 pandemic, which requires online learning, is increasing academic procrastination. Academic procrastination has a serious impact on academic achievement, so it needs strategy to reduce academic procrastination. This study aims to determine the use of combinative classroom management to reduce student academic procrastination. This research is to review research. Literature searches related to strategies to reduce academic procrastination were carried out through Google Scholar and ScienceDirect. The literature relevant to the research objectives was analyzed using hermeneutic techniques. The results of the literature review indicate the need for the use of combinative class management (behavioristic and humanistic approaches). The results of this review literature review can be use as a reference in arranging classes to reduce academic procrastination in students, either during the Covid-19 pandemic or after.Keywords: Combinative classroom management; student academic procrastination; strategies to reduce academic procrastinatio

An Analysis of Student Learning Strategies in High Enrollment Computer-Based College Courses

The subject of this thesis is an observational investigation of the effect on outcomes of three behavior patterns students follow during the course of a fourteen week semester in two large, STEM (Science, Technology, Engineering, and Math) college courses: an introductory Computer Science course and an introductory Chemistry course. Both courses employed a computer-based system that recorded student textbook and homework activity. The behavior patterns we investigated are: 1) “book-first”- reading and interacting with the textbook material before working homework problems, 2) “infrequent-session”- long stretches of time between short working sessions with book or chapter homework material, and 3) “working-late”- submitting homework close to the due date. In order to assess the effect of these patterns on outcomes, i.e. final exam scores, we created features to measure the amount of textbook interaction, the relative length of time between work sessions, and the amount of work submitted close to due dates. Our analysis showed a statistically significant, positive effect of following a book-first strategy for students in all courses, and a greater positive effect for novice students. For the second study, we found evidence that the pattern of short working sessions with long intervals was related to lower exam scores. In the third study, we found a negative effect of late work on final exam scores. We found novice students were more negatively affected than experienced students

Recent Advances in Academic Performance Analysis

[EN] Academic performance analysis has gained popularity in the past decade. Using various prediction and classification methods, researchers aim to provide clues to help students to improve their performance, and to assist educational institutions to improve quality and make better administrative decisions. This work provides a brief survey of 56 papers related to academic performance prediction, published in 2019 and 2020. Statistics and analysis on the prediction target categories, the target population size, prediction and classification methodologies used, and evaluation metrics are presented. It is found that the most commonly used techniques are decision tree, ensemble methods, and neural networks. Futhermore, these techniques also give the highest accuracy in their target prediction.Zhang, L.; Li, KF.; Bourguiba, I. (2021). Recent Advances in Academic Performance Analysis. En 7th International Conference on Higher Education Advances (HEAd'21). Editorial Universitat Politècnica de València. 607-614. https://doi.org/10.4995/HEAd21.2021.13196OCS60761

Online Learning in Higher Education During A Global Pandemic: An Explorative Study On Norwegian Students

Through an exploratory study we aimed to address challenging factors related to learning during the Coronavirus pandemic. We have investigated and identified procrastination, self-regulation, and exam anxiety as important factors associated with learning success. Since the educational sector has shifted radically towards online learning, we have additionally examined previous literature related to learning analytics, learning during disasters, and online learning. To address our research objective, we initially applied a survey to map out procrastinators and non- procrastinators to include for our semi-structured interviews with students. We additionally conducted a small sample of interviews with teachers, and one teaching technology manager, to acquire their perspective on the current situation. While prior studies under Covid-19 found that online learning has been perceived positive by students, our findings revealed challenges related to engagement in online lectures, and thus, suggesting that engagement is not properly facilitated through the current learning management systems. This paucity of engagement is further argued to reduce the students’ overall learning outcome in terms of both practical knowledge and informal understanding of subjects. However, it does not reflect on the grades as the evaluation system has become more lenient. Our findings further revealed significant differences amid procrastinators and non-procrastinators when investigating the students’ study behavior and habits, and we see that procrastinators in higher degree encounter challenges related to motivation, allocating time to study, and structure, as opposed to non-procrastinators. Nevertheless, our findings reveal that the teachers are not able to sufficiently follow-up students-at-risk themselves because of time constraints and limited resources, and a lack of an appropriate framework is hindering the university to adequately adopt learning analytics. Keywords: Online Learning, Covid-19, Learning Analytics, Procrastination, Self-regulation, Test Anxiet