The risk of multitasking is high in online surveys. However, knowledge on the effects of multitasking on answer quality is sparse and based on suboptimal approaches. Research reports inconclusive results concerning the consequences of multitasking on task performance. However, studies suggest that especially sequential-multitasking activities are…

Conducting high-quality overviews of reviews (OoR) is time-consuming. Because the quality of systematic reviews (SRs) varies, it is necessary to critically appraise SRs when conducting an OoR. A well-established appraisal tool is A Measurement Tool to Assess Systematic Reviews (AMSTAR) 2, which takes about 15-32 min per application. To save time,…

Educators and researchers have analyzed various image data acquired from teaching and learning, such as images of learning materials, classroom dynamics, students' drawings, etc. However, this approach is labour-intensive and time-consuming, limiting its scalability and efficiency. The recent development in the Visual Question Answering (VQA)…

This study builds on the long-identified gap between the marketing industry and marketing academia. The research investigates the relationship between resource-based factors and marketing faculty's intention to use marketing analytics technology. The theoretical framework for this study is the concerns-based adoption model (CBAM). Resource-based…

Faculty teaching data analytics at undergraduate level are often faced with the tension created by student under-preparedness, the demands of the course, and time constraints. How do faculty close this gap? In this paper, we propose the use of flow diagramming as an accessible method for interpreting regression analyses, in ways that are time…

Research productivity plays an important role in the prestige and reputation among higher education institutions. However, the time spent to do research among Filipino academics is the most pressing issue since they can barely meet the requirement for research productivity. Further, the lack of time for data gathering aggravated the drawbacks for…

In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

Universities are under pressure to produce work-ready graduates. This study analyzed 130 job advertisements to identify skills required by environmental science employers in Australia. For degree-related criteria, the most frequently required were content knowledge, a tertiary qualification and experience. Other desired skills were an…

It is challenging for students to plan their work sessions in online environments, as it is very difficult to make estimates on how much material there is to cover. In order to simplify this estimation, we have extended the Keystroke-level analysis model with individual reading speed of text, figures, and questions. This was used to estimate how…

Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students' timing of…

This chapter reviews actionable data use--both as an umbrella term and as a specific concept--developed in three different traditions that data/information can inform and guide P-20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning…

This study describes the process of constructing proxy variables from recorded log data within a Learning Management System (LMS), which represents adult learners' time management strategies in an online course. Based on previous research, three variables of total login time, login frequency, and regularity of login interval were selected as…

The practice and application of education data mining and learning analytics has become the focus of educational researchers. However, it is still a difficult task to explore the law of group learning and the characteristics of individual learning. In this study, the online learning logs of 1,088 students from 22 classes were analyzed from the…

This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…