Standing on the shoulders of giants: Online formative assessments as the foundation for predictive learning analytics models

Abstract

As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data-driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their attention to learning analytics (LA) applications using educational data mining techniques. Numerous LA models have been proposed to predict student achievement in university courses. To design predictive LA models, researchers often follow a data-driven approach that prioritizes prediction accuracy while sacrificing theoretical links to learning theory and its pedagogical implications. In this study, we argue that instead of complex variables (e.g., event logs, clickstream data, timestamps of learning activities), data extracted from online formative assessments should be the starting point for building predictive LA models. Using the LMS data from multiple offerings of an asynchronous undergraduate course, we analysed the utility of online formative assessments in predicting students' final course performance. Our findings showed that the features extracted from online formative assessments (e.g., completion, timestamps and scores) served as strong and significant predictors of students' final course performance. Scores from online formative assessments were consistently the strongest predictor of student performance across the three sections

Learning analytics and assessment: Emerging research trends, promises and future opportunities

“…In the first group of studies, the authors harnessed learning analytics to support the assessment of course content comprehension. Bulut et al (2022) and Emerson et al (2022) demonstrated that combining learner trace-data and data from formative assessments can be a viable approach to predicting learning performance of university students. For instance, Bulut et al (2022) found that data gathered in online formative assessment activities, for example, scores and time features, can predict student performance in midterm and final exams.…”

“…Bulut et al (2022) and Emerson et al (2022) demonstrated that combining learner trace-data and data from formative assessments can be a viable approach to predicting learning performance of university students. For instance, Bulut et al (2022) found that data gathered in online formative assessment activities, for example, scores and time features, can predict student performance in midterm and final exams. Emerson et al (2022) combined student log data collected in a game-based learning environment and the content from the post-test assessment questions, and developed an early prediction model of student achievements on a post-test.…”

Mathematics Teachers' Self-Reported Practices of Formative Assessments in Teaching Mathematics Online

“…The primary focus of online assessment is to promote formative assessment strategies (Alruwais et al, 2018). Formative assessment could provide more information about students' understanding and misconceptions, and the scores from an online formative assessment are the strongest predictor of student performance (Bulut, et al 2023). The instructor could have sufficient time to reflect on the information, give appropriate feedback, and modify teaching-learning activities (Schroeder & Dorn, 2016).…”

Evaluating the Impact of Learning Management Systems in Geographical Education in Primary School: An Experimental Study on the Importance of Learning Analytics-Based Feedback

“…This adaptation involves a departure from traditional summative assessment approaches, which concentrate exclusively on the final outcome, focusing instead on assessing the learning process itself [4,9]. Authors such as Bulut et al [10] and Gašević et al [11] support this approach, emphasizing the importance of redefining assessment strategies to reflect the dynamic and multifaceted nature of contemporary learning environments more accurately.…”