Smartlearn: Predicting learning performance and discovering smart learning strategies in flipped classroom

Abstract

In flipped classroom, students are expected to learn new contents in online learning system before attending offline classes to reinforce their knowledge. This online and offline blended education model has become more and more popular. However, spending more time to actively engage in online learning does not result in better learning performance, so that how to wisely arrange online learning plan is a big challenge. In this paper, we build a LASSO model to accurately predict students' performance in course projects and their final grade by online learning behaviour data in flipped classroom. The LASSO selected features show that learning online between first and second flipped classes after midnight, and during the second flipped class would benefit students' project performance but studying one day before the examination and studying at night is counterproductive. Our results provide novel insight into guiding students to learn wisely and perform better in flipped classroom.

Investigating Factors Based on an Extended UTAUT Model to Confirm Computer Science Instructors’ Behavioural Intention to Adopt the Flipped Classroom

“…The second category is the participants who noted that the flipped classroom had been adopted by other colleagues prior to them (FC- Ps 3,4,7,8,10,11,12,13 and 14). According to FC-P7, "There are many lecturers use the flipped classroom in my university, including some lecturer in my department."…”

“…According to [2] "flipped or inverted classrooms are a blended learning modality, whereby traditional in-class lectures and homework exercises are reversed". The method was introduced by [4] as a student-centred approach in which learners explore learning materials through an online learning system and then attend offline flipped classes. Furthermore, [1] emphasized the importance of achieving a balance between face-to-face and online courses in a blended learning model in order to achieve the educational satisfaction of learners as well as educators.…”

Learning Analytics in Flipped Classrooms: A Scoping Review

“…Stemming from the previous category, we also identified 13 studies that specifically focus on online performance leading to an evaluation or assessment. Metrics such as the completion rate and success rate in the course are taken into account (Kaw, et al 2019;Yang, Wu and Cao, 2016), as well as summative evaluations (Smallhorn, 2017). A single study integrated peer evaluation (Fernández, Merino and Kloos, 2019).…”

“…Some studies also underlined negative or non-significant results. For example, one pointed no statistical difference between passing and failing students (Gelan et al, 2018), while another one no correlation between success rate and time spent online (Yang, Wu and Cao, 2016). One had a disappointing pass rate due to lack of quality engagement with the learning materials (Lei et al, 2017), or no effect for the highest performing students (Jovanovic et al, 2017).…”

The Latent of Student Learning Analytic with K-mean Clustering for Student Behaviour Classification

“…MOOCs (Massive Open Online Courses) enable students to distribute and pathway toward their awareness. Authors presenting big data analysis technique toward education collection to reveal how the communication, unspecified association, and unseen designs among between students that utilized MOOCs [2]. The analyzed information will be used to study pattern learning behavior of student that can bring a feedback toward the courses and teacher in the forthcoming.…”