What Learning Analytics-Based Prediction Models Tell Us about Feedback Preferences of Students

By Nguyen, Quan; Tempelaar, Dirk T. et al. | Quarterly Review of Distance Education, January 1, 2016 | Go to article overview

What Learning Analytics-Based Prediction Models Tell Us about Feedback Preferences of Students


Nguyen, Quan, Tempelaar, Dirk T., Rienties, Bart, Giesbers, Bas, Quarterly Review of Distance Education


INTRODUCTION

In educational settings, an enormous volume of potentially valuable information is generated by both students and educators. Such information may include academic performance, tracking data from online learning environments, e-mails, and social network data. In recent years, the term "learning analytics" has emerged as educational institutions and corporate learning started to harness this wealth of information to provide real-time feedback to students while offering valuable insights for educators to improve teaching quality (Siemens, Dawson, & Lynch, 2013). In the corporate world, learning analytics (LA) can help learning and development of professionals by identifying successful learning activities and patterns, with clear indications of the learning progress of its employees. In a higher education context, students and teachers may benefit from personalized and adaptive learning experiences (Knewton, 2016). To better catalyze the processes of learning for individuals and collectives, Buckingham Shum and Crick (2012) have proposed a dispositional learning analytics infrastructure that combines learning activity generated data with learning dispositions, values and attitudes measured through self-report surveys which are fed back to students and teachers through visual analytics. Tempelaar, Rienties, and Giesbers (2015) have investigated the predictive power of learning dispositions, outcomes of continuous formative assessments, and other system-generated data on modeling student performance and their potential to generate informative feedback. The study found that computer-assisted formative assessments can best detect underperforming student and academic performance.

In learning theory, monitoring and evaluation play a crucial role, as they provide feedback on how activities coordinate across several stages of studies (task definition, goal setting and planning, and enacting study tactics and strategies) (Winne & Hadwin, 1998). Feedback assesses the level of understanding of learners and can provide cues for reinforcement. In a meta-study by Hattie (2013), feedback is considered one of the most powerful tools in enhancing the learning experience. In the past, traditional formal feedback is limited to taking the form of a grade, which is available only after finishing all learning activities. However, the involvement of educational technology allows us to gather feedback on learning-in-progress activities, which provides a real-time assessment to both students and teachers. For instance, a study by Duffy and Azevedo (2015) revealed that students in the "prompt and feedback" condition deployed more self-regulated learning strategies and spent more time viewing relevant science material compared to students in the control condition, in which learners did not receive any support. Additionally, McLaren, van Gog, Ganoe, Karabinos, and Yaron (2016) categorized different feedback modes into worked examples, erroneous examples, tutored problems, and problem solving. Their study showed clear efficiency benefits of the use of worked examples in a web-based learning environment: equal levels of test performance were achieved, with significantly less investment of time and effort during learning. Given the importance of feedback and the advancement in assessment technology, the investigation of the effects of feedback use by students on their academic performance suggests being a promising research trajectory in learning analytics.

This study examines how learning dispositions and feedback preferences affect academic performance. The article is organized as follows. The next section (Section 2) introduces the context of the study and its instruments. This is followed by Section 3, which presents the results, and is followed by the discussion in Section 4. Finally, Section 5 concludes the study and discusses the implications of big data in education and LA for online learners/instructors, and how this study bridges the gap between existing LA literature and pedagogy. …

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