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Sentiment Analysis in MOOC Discussion Forums: What does it tell us?
"... Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students ’ trending opinions towards the course and major ..."
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Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students ’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behav-iors are used in practice across three MOOCs. Implications for research and practice are discussed.
Modeling learners’ social centrality and performance . . .
"... There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs). However, despite this interest in learning at scale, there has been limited work investigating the impact MOOCs can play on student learning. In this study, we adopt a novel approach, using langua ..."
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There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs). However, despite this interest in learning at scale, there has been limited work investigating the impact MOOCs can play on student learning. In this study, we adopt a novel approach, using language and discourse as a tool to explore its association with two established measures related to learning: traditional academic performance and social centrality. We demonstrate how characteristics of language diagnostically reveal the performance and social position of learners as they interact in a MOOC. We use Coh-Metrix, a theoretically grounded, computational linguistic modeling tool, to explore students’ forum postings across five potent discourse dimensions. Using a Social Network Analysis (SNA) methodology, we determine learners’ social centrality.
Learning instructor intervention from MOOC forums
- In Proceedings of the 8th International Conference on Educational Data Mining
, 2015
"... With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of instructor inter-vention. Using a large sample of forum data culled from 61 courses, we design a binary classifier to pr ..."
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With large student enrollment, MOOC instructors face the unique challenge in deciding when to intervene in forum discussions with their limited bandwidth. We study this problem of instructor inter-vention. Using a large sample of forum data culled from 61 courses, we design a binary classifier to predict whether an instructor should intervene in a discussion thread or not. By incorporating novel in-formation about a forum’s type into the classification process, we improve significantly over the previous state-of-the-art. We show how difficult this decision problem is in the real world by validating against indicative human judgment, and empirically show the problem’s sensitivity to instructors ’ intervention prefer-ences. We conclude this paper with our take on the future research issues in intervention.
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.
Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses
"... MOOCs attract diverse users with varying habits. Identify-ing those patterns through clickstream analysis could enable more effective personalized support for student information seeking and learning in that online context. We propose a novel method to characterize types of sessions in MOOCs by mini ..."
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MOOCs attract diverse users with varying habits. Identify-ing those patterns through clickstream analysis could enable more effective personalized support for student information seeking and learning in that online context. We propose a novel method to characterize types of sessions in MOOCs by mining the habitual behaviors of students within individual sessions. We model learning sessions as a distribution of ac-tivities and activity sequences with a topical N-gram model. The representation offers insights into what groupings of ha-bitual student behaviors are associated with higher or lower success in the course. We also investigate how context in-formation, such as time of day or a user’s demographic in-formation, is associated with the types of learning sessions.
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.
