Bart Pursel

An Analysis of MOOC Discussion Forum Interactions from the Most Active Users

Many massive open online courses (MOOCs) offer mainly video-based lectures, which limits the opportunity for interactions and communications 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 identified. We then employ different measures for evaluating whether those active users have more influence on overall forum activities. We further analyze forum 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 effect on the discussions. Implications for MOOC student engagement and retention are discussed.

Concept Hierarchy Extraction from Textbooks

Concept hierarchies have been useful tools for presenting and organizing knowledge. With the rapid growth in the number of online knowledge resources, automatic concept hierarchy extraction is increasingly attractive. Here, we focus on concept extraction from textbooks based on the knowledge in Wikipedia. Given a book, we extract important concepts in each book chapter using Wikipedia as a resource and from this construct a concept hierarchy for that book. We define local and global features that capture both the local relatedness and global coherence embedded in that textbook. In order to evaluate the proposed features and extracted concept hierarchies, we manually construct concept hierarchies for three well used textbooks by labeling important concepts for each book chapter. Experiments show that our proposed local and global features achieve better performance than using only keyphrases to construct the concept hierarchies. Moreover, we observe that incorporating global features can improve the concept ranking precision and reaffirms the global coherence in the book.

Analyzing MOOC discussion forum messages to identify cognitive learning information exchanges

While discussion forums in online courses have been studied in the past, no one has proposed a model linking messages in discussion forums to a learning taxonomy, even though forums are widely used as educational tools in online courses. In this research, we view forums as information seeking events and use a keyword taxonomy approach to analyze a large amount of MOOC forum data to identify the types of learning interactions taking place in forum conversations. Using 51,761 forum messages from 8,169 forum threads from a MOOC with a 50,000+ enrollment, messages are analyzed based on levels of Blooms Taxonomy to categorize the scholarly discourse. The results of this research show that interactions within MOOC discussion forums are a learning process with unique characteristics specific to particular cognitive learning levels. Results also imply that different types of forum interactions have characteristics relevant to particular learning levels, and the volume of higher levels of cognitive learning incidents increase as the course progresses.

Using Prerequisites to Extract Concept Maps fromTextbooks

We present a framework for constructing a specific type of knowledge graph, a concept map from textbooks. Using Wikipedia, we derive prerequisite relations among these concepts. A traditional approach for concept map extraction consists of two sub-problems: key concept extraction and concept relationship identification. Previous work for the most part had considered these two sub-problems independently. We propose a framework that jointly optimizes these sub-problems and investigates methods that identify concept relationships. Experiments on concept maps that are manually extracted in six educational areas (computer networks, macroeconomics, precalculus, databases, physics, and geometry) show that our model outperforms supervised learning baselines that solve the two sub-problems separately. Moreover, we observe that incorporating textbook information helps with concept map extraction.

BBookX: An Automatic Book Creation Framework

As more educational resources become available online, it is possible to acquire more up-to-date knowledge and information. We propose BBookX, a novel computer facilitated system that automatically and collaboratively builds free open online books using publicly available educational resources such as Wikipedia. BBookX has two separate components: one creates an open version of existing books by linking different book chapters to Wikipedia articles, while another with an interactive user interface supports interactive real-time book creation where users are allowed to modify a generated book from explicit feedback.

An Analysis of Cognitive Learning Context in MOOC Forum Messages

In this research, we analyze a large number of discussions of forum messages from three MOOC courses using a keyword taxonomy approach to identify learning processes occurring among the students. We conduct analysis on more than 100,000 forum messages from 14,647 forum threads from three MOOCs, with a combined 200,000+ enrollment. We map messages to six levels of Blooms Taxonomy for cognitive learning. The results of this research indicate that learning processes of particular cognitive learning levels could be observed within discussions on MOOC forums. Results imply that different types of forum communications have features associated to particular learning levels, and the volume of higher levels of cognitive learning domains increase as the course progresses.

A semantic network model for measuring engagement and performance in online learning platforms

Due to the increasing global availability of the internet, online learning platforms such as Massive Open Online Courses (MOOCs), have become a new paradigm for distance learning in engineering education. While interactions between instructors and students are readily observable in a physical classroom environment, monitoring student engagement is challenging in MOOCs. Monitoring student engagement and measuring its impact on student performance are important for MOOC instructors, who are focused on improving the quality of their courses. The authors of this work present a semantic network model for measuring the different word associations between instructors and students in order to measure student engagement in MOOCs. Correlation analysis is then performed for identifying how student engagement in MOOCs affect student performance. Real‐world MOOC transcripts and MOOC discussion forum data are used to evaluate the effectiveness of this research.

Distractor Generation with Generative Adversarial Nets for Automatically Creating Fill-in-the-blank Questions

Distractor generation is a crucial step for fill-in-the-blank question generation. We propose a generative model learned from training generative adversarial nets (GANs) to create useful distractors. Our method utilizes only context information and does not use the correct answer, which is completely different from previous Ontology-based or similarity-based approaches. Trained on the Wikipedia corpus, the proposed model is able to predict Wiki entities as distractors. Our method is evaluated on two biology question datasets collected from Wikipedia and actual college-level exams. Experimental results show that our context-based method achieves comparable performance to a frequently used word2vec-based method for the Wiki dataset. In addition, we propose a second-stage learner to combine the strengths of the two methods, which further improves the performance on both datasets, with 51.7% and 48.4% of generated distractors being acceptable.