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Identifying At-Risk Students in Massive Open Online Courses
"... Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their suc-cesses, a major problem faced by MOOCs is low com-pletion rates. In this paper, we exp ..."
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Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Despite their suc-cesses, a major problem faced by MOOCs is low com-pletion rates. In this paper, we explore the accurate early identification of students who are at risk of not complet-ing courses. We build predictive models weekly, over multiple offerings of a course. Furthermore, we envision student interventions that present meaningful probabil-ities of failure, enacted only for marginal students. To be effective, predicted probabilities must be both well-calibrated and smoothed across weeks. Based on logis-tic regression, we propose two transfer learning algo-rithms to trade-off smoothness and accuracy by adding a regularization term to minimize the difference of failure probabilities between consecutive weeks. Experimental results on two offerings of a Coursera MOOC establish the effectiveness of our algorithms.
SUPPORTIVE TECHNOLOGIES FOR GROUP DISCUSSION IN MOOCS
"... This Article is brought to you for free and open access by ScholarWorks at UMass Boston. It has been accepted for inclusion in Current Issues in Emerging eLearning by an authorized administrator of ScholarWorks at UMass Boston. For more information, please contact library.uasc@umb.edu. ..."
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This Article is brought to you for free and open access by ScholarWorks at UMass Boston. It has been accepted for inclusion in Current Issues in Emerging eLearning by an authorized administrator of ScholarWorks at UMass Boston. For more information, please contact library.uasc@umb.edu.
Data mining and education
"... Abstract An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from many kinds of educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, languag ..."
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Abstract An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from many kinds of educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss 1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, 2) how better cognitive models can be discovered from data and used to improve instruction, 3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and 4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or discussion board.
YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips
"... ABSTRACT In Massive Open Online Courses (MOOCs), struggling learners often seek help by posting questions in discussion forums. Unfortunately, given the large volume of discussion in MOOCs, instructors may overlook these learners' posts, detrimentally impacting the learning process and exacerb ..."
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ABSTRACT In Massive Open Online Courses (MOOCs), struggling learners often seek help by posting questions in discussion forums. Unfortunately, given the large volume of discussion in MOOCs, instructors may overlook these learners' posts, detrimentally impacting the learning process and exacerbating attrition. In this paper, we present YouEDU, an instructional aid that automatically detects and addresses confusion in forum posts. Leveraging our Stanford MOOCPosts corpus, we train a set of classifiers to classify forum posts across multiple dimensions. In particular, classifiers that target sentiment, urgency, and other descriptive variables inform a single classifier that detects confusion. We then employ information retrieval techniques to map confused posts to minute-resolution clips from course videos; the ranking over these clips accounts for textual similarity between posts and closed captions. We measure the performance of our classification model in multiple educational contexts, exploring the nature of confusion within each; we also evaluate the relevancy of materials returned by our ranking algorithm. Experimental results demonstrate that YouEDU achieves both its goals, paving the way for intelligent intervention systems in MOOC discussion forums.
An Analysis of MOOC Discussion Forum Interactions from the Most Active Users
"... Abstract. Many massive open online courses (MOOCs) offer mainly video-based lectures, which limits the opportunity for interactions and communica-tions among students and instructors. Thus, the discussion forums of MOOC become indispensable in providing a platform for facilitating interactions and c ..."
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Abstract. Many massive open online courses (MOOCs) offer mainly video-based lectures, which limits the opportunity for interactions and communica-tions among students and instructors. Thus, the discussion forums of MOOC become indispensable in providing a platform for facilitating interactions and communications. In this research, discussion forum users who continually and actively participate in the forum discussions throughout the course are identi-fied. We then employ different measures for evaluating whether those active users have more influence on overall forum activities. We further analyze fo-rum votes, both positive and negative, on posts and comments to verify if active users make positive contributions to the course conversations. Based the result of analysis, users who constantly participate in forum discussions are identified as statistically more influential users, and these users also produce a positive ef-fect on the discussions. Implications for MOOC student engagement and reten-tion are discussed.
Semi-automatic annotation of MOOC forum posts
"... Abstract. Massive online open courses ’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying fac-tors. One of the reasons which hinders the analysis is the amount of manual work required ..."
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Abstract. Massive online open courses ’ (MOOCs’) students who use discussion forums have higher chances of finishing the course. However, little research has been conducted for understanding the underlying fac-tors. One of the reasons which hinders the analysis is the amount of manual work required for annotating posts. In this paper we use ma-chine learning techniques to extrapolate small set of annotations to the whole forum. These annotations not only allow MOOC producers to sum-marize the state of the forum, but they also allow researchers to deeper understand the role of the forum in the learning process. 1
Capturing “attrition intensifying ” structural traits from didactic interaction sequences of MOOC learners
"... This work is an attempt to discover hidden structural configurations in learning activ-ity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video click-stream interactions and forum activities, we seek to fundamentally understand traits that are pr ..."
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This work is an attempt to discover hidden structural configurations in learning activ-ity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video click-stream interactions and forum activities, we seek to fundamentally understand traits that are predictive of decreasing engage-ment over time. Grounded in the inter-disciplinary field of network science, we follow a graph based approach to success-fully extract indicators of active and pas-sive MOOC participation that reflect per-sistence and regularity in the overall in-teraction footprint. Using these rich edu-cational semantics, we focus on the prob-lem of predicting student attrition, one of the major highlights of MOOC literature in the recent years. Our results indicate an improvement over a baseline ngram based approach in capturing “attrition intensify-ing ” features from the learning activities that MOOC learners engage in. Implica-tions for some compelling future research are discussed. 1
Point-of-View Mining and Cognitive Presence in MOOCs: A (Computational) Linguistics Perspective
"... This paper explores the cognitive presence of the learners in MOOCs through using a (computational) linguistic analysis of the learners ’ Point-of-View as an indicator for cognitive presence. The linguistic analysis of the written language as a medium of interaction by the students in the context of ..."
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This paper explores the cognitive presence of the learners in MOOCs through using a (computational) linguistic analysis of the learners ’ Point-of-View as an indicator for cognitive presence. The linguistic analysis of the written language as a medium of interaction by the students in the context of MOOCs shows hallmarks of cognitive disengagement and low cognitive presence by the learners.
