Measuring Behaviors and Identifying Indicators of Self-Regulation in Computer-Assisted Language Learning Courses

The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning

Supporting “time awareness” in self-regulated learning: How do students allocate time during exam preparation?

The development of technology enables diverse learning experiences nowadays, which shows the importance of learners’ self-regulated skills at the same time. Particularly, the ability to allocate time properly becomes an issue for learners since time is a resource owned by all of them. However, they tend to struggle to manage their time well due to the lack of awareness of its existence. This study, hence, aims to reveal how learners allocate their time and evaluate the effectiveness of the time allocation by examining its effects on learners’ performance. We collect the learning logs of 116 seventh-graders from the online learning system implemented in a Japanese public junior high school. We look at the data in the time window of 34 days before the regular exam. Even though clustering techniques as a Learning Analytics method help identify different groups of learners, it is seldom applied to group students’ learning patterns with different levels of indicators extracted from their learning process data. In this study, we adopt the method to cluster students’ patterns of time allocation and find that better performance can result from the consistency of study time throughout the exam preparation period. Practical suggestions are then proposed for different roles involved in digital learning environments to facilitate students’ time management. Collectively, this study is expected to make contributions to smart learning environments supporting self-regulated learning in the digital era

Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis

Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system

Measuring Behaviors and Identifying Indicators of Self-Regulation in Computer-Assisted Language Learning Courses

The aim of this research is to measure self-regulated behavior and identify significant behavioral indicators in computer-assisted language learning courses. The behavioral measures were based on log data from 2454 freshman university students from Art and Science departments for 1 year. These measures reflected the degree of self-regulation, including anti-procrastination, irregularity of study interval, and pacing. Clustering analysis was conducted to identify typical patterns of learning pace, and hierarchical regression analysis was performed to examine significant behavioral indicators in the online course. The results of learning pace clustering analysis revealed that the final course point average in different clusters increased with the number of completed quizzes, and students who had procrastination behavior were more likely to achieve lower final course points. Furthermore, the number of completed quizzes and study interval irregularity were strong predictors of course performance in the regression model. It clearly indicated the importance of self-regulation skill, in particular completion of assigned tasks and regular learning

An exploration of the mediating effects of a digital, mobile vocabulary learning tool and device use on Gulf Arab learners’ receptive vocabulary knowledge and capacity for self-regulated learning.

Receptive knowledge of the meanings of the first 3,000 most frequent word families in English is a vital pre-requisite for enabling academic reading and contributing to academic success in higher education where English is the medium of instruction. While many English foundation programmes include frequency-based word lists for their students to learn, learning gains made by students have frequently proven to be disappointing and little attention has been paid to the technological interventions to learn these words. In addition, little consideration has been given to the negative aspects of smartphone use to learn these words. In this naturalistic, mixed-methods study, I explore the mediating effects of using an off-the-shelf, digital vocabulary learning tool in out-of-class settings on the receptive vocabulary knowledge of students in the United Arab Emirates. I also examine how the same tool mediates the students’ capacity for self-regulation and whether different devices had any effect on this, both through a self-reported, online survey tool and pair-depth interviews that aim to capture rich, qualitative data about the learners’ own perceptions. Overall, the findings show that students’ receptive vocabulary knowledge increased, but their self-reported capacity for self-regulated vocabulary learning through technology showed no change. In terms of devices, many students preferred to use the web-based version of the digital tool on their laptops rather than the mobile application on their smartphones. While students saw the laptop as a serious learning device that better enabled self-regulated vocabulary learning, the smartphone is seen predominantly as a communication and entertainment device to access social media, which depleted students’ ability to self-regulate their vocabulary learning, particularly their ability to remain committed to their learning goals. Device control is therefore an important dimension of self-regulated, mobile vocabulary learning, which needs to be considered in future research in this field