We aim to identify the engagement strategies that higher education students, engaging in emergency online learning in low-resource settings, perceive to be effective. We conducted a sequential mixed-methods study based on Moore’s interaction framework for distance education. We administered a questionnaire to 313 students engaging in emergency online learning in low-resource settings to examine their perceptions of different engagement strategies. Our results showed that student–content engagement strategies, e.g., screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student–teacher strategies, e.g., Q and A sessions and reminders. Student–student strategies, e.g., group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies varies based on the students’ gender and technology access. To support instructors, instructional designers, and researchers, we propose a 10-level guide for engaging students during emergency online classes in low-resource settings.

This study aims to identify the bibliometric outcomes of articles and research trends in specific areas. This study revealed research on the use of e-books for EFL education published in selected journals of the Web of Science from 2011 to 2020, and discusses the trends of the top-cited countries, journals, the most used keywords, and the popular subjects, learning strategies, target skills, and the research outcomes. A total of 53 selected articles were manually and systematically analyzed. The results revealed that the most productive country was Taiwan, and the most cited journal was Computers & Education. The most used keywords were “engagement.” The bibliometric analysis can be reflected in the content analysis as preschoolers and elementary school students were the most common participants, guided learning were the most frequently used strategy, and the main target skill was reading. This review study provides an overview of the research agenda for the last 10 years, and highlights the trends in the field by analyzing relevant past research. When the use of e-books in EFL education studies was examined, reading comprehension, teaching/learning strategies, and learning engagement were found to be crucial in the studies. Therefore, there is a need to develop more useful features of e-books to support EFL teaching practices. It is therefore suggested that future research focus more on pedagogical aspects and e-book feature development.

While data privacy is a key aspect of Learning Analytics, it often creates difficulty when promoting research into underexplored contexts as it limits data sharing. To overcome this problem, the generation of synthetic data has been proposed and discussed within the LA community. However, there has been little work that has explored the use of synthetic data in real-world situations. This research examines the effectiveness of using synthetic data for training academic performance prediction models, and the challenges and limitations of using the proposed data sharing method. To evaluate the effectiveness of the method, we generate synthetic data from a private dataset, and distribute it to the participants of a data challenge to train prediction models. Participants submitted their models as docker containers for evaluation and ranking on holdout synthetic data. A post-hoc analysis was conducted on the top 10 participant’s models by comparing the evaluation of their performance on synthetic and private validation datasets. Several models trained on synthetic data were found to perform significantly poorer when applied to the non-synthetic private dataset. The main contribution of this research is to understand the challenges and limitations of applying predictive models trained on synthetic data in real-world situations. Due to these challenges, the paper recommends model designs that can inform future successful adoption of synthetic data in real-world educational data systems.

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.