Modeling and Predicting Learning Behavior in MOOCs

Abstract

Massive Open Online Courses (MOOCs), which collect complete records of all student interactions in an online learning environment, offer us an unprecedented opportunity to analyze students' learning behavior at a very fine granularity than ever before.Using dataset from xuetangX, one of the largest MOOCs from China, we analyze key factors that influence students' engagement in MOOCs and study to what extent we could infer a student's learning effectiveness. We observe significant behavioral heterogeneity in students' course selection as well as their learning patterns. For example, students who exert higher effort and ask more questions are not necessarily more likely to get certificates. Additionally, the probability that a student obtains the course certificate increases dramatically (3× higher) when she has one or more "certificate friends".Moreover, we develop a unified model to predict students' learning effectiveness, by incorporating user demographics, forum activities, and learning behavior. We demonstrate that the proposed model significantly outperforms (+2.03-9.03% by F1-score) several alternative methods in predicting students' performance on assignments and course certificates. The model is flexible and can be applied to various settings. For example, we are deploying a new feature into xuetangX to help teachers dynamically optimize the teaching process.

Exploring Students’ Engagement Patterns in SPOC Forums and their Association with Course Performance

“…The increased use of SPOC forums has contributed to the generation of massive interactive data in higher education. Within the forum data, the hidden engagement patterns involving interactive behaviors and emotional states have been considered as critical factors indicating learning performance (Kent, Laslo, & Rafaeli, 2016;Phan, McNeil, & Robin, 2016;Qiu et al, 2016;Tucker, Pursel, & Divinsky, 2014;Yang, Kraut, & Rosé, 2016;Yang, Wen, Howley, Kraut, & Rose, 2015). Especially, interactive postings and average depth in threads were considered to be correlated with examination' grades (Kent et al, 2016).…”

Machine learning approaches to predict learning outcomes in Massive open online courses

“…Massive Open Online Courses (MOOCs) have become an alternative educational platform that allows learners from dispersed geographic locations access the same quality of learning through the web [1]. Coursera, HarvardX, and Khan Academy are some examples of MOOCs.…”

“…Coursera, HarvardX, and Khan Academy are some examples of MOOCs. Since 2012, MOOCs modalities have received widespread usage by top Universities [1]. Investigations undertaken by such institutions indicated that the use of MOOCs have attracted many participants towards engagement in the space of courses offered, due to the removal of financial, geographical, and educational barriers [1].…”

“…Investigations undertaken by such institutions indicated that the use of MOOCs have attracted many participants towards engagement in the space of courses offered, due to the removal of financial, geographical, and educational barriers [1]. A large volume of data can be collected and captured from MOOCs platforms during Student interaction with learning activities, such as viewing of video lectures, undertaking of quizzes, posting in discussion forums, and interacting with the courseware [1,3]. Data captured from MOOCs can provide valuable information for educators by analysing the patterns present in the behaviour of learners [2,3].…”

Learning Effect Analysis Based on Association Rules in Online Education

“…The "China Basic Education Big Data Development Blue Book (2015)" Conference and Education Big Data Summit Forum held in Beijing Normal University in 2016 [2]. In addition, according to the statistics, Coursera, edX, and Udacity which are three pioneer MOOC platforms provide thousands of courses and draw more than 100,000 registrants each course [5,6]. And Tsinghua University also officially launched the first Chinese version of the MOOC platform -"xuetangX" [4,8].…”