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With the emergence of MOOCs, it becomes crucial to automate the process of a course design to accommodate the diverse learning demands of students. Modeling the relationships among educational topics is a fundamental first step for automating curriculum planning and course design. In this paper, we introduce "Topic Transition Map" (TTM), a general structure that models the content of MOOCs at the topic level. TTMs capture the various ways instructors organize topics in their courses by modeling the transitions between topics. We investigate and analyze four different methods that can be exploited to learn the Topic Transition Map: (1) Pairwise Constrained K-Means, (2) Mixture of Unigram Language Model, (3) Hidden Markov Mixture Model, and (4) Structural Topic Model. To evaluated the effectiveness of these methods, we qualitatively compare the topic transition maps generated by each model and investigate how the Topic Transition Map can be used in three sequencing tasks: (1) determining the correct sequence, (2) predicting the next lecture, and (3) predicting the sequence of lectures. Our evaluation revealed that PCK-Means has the highest performance in the first task, HMMULM outperforms other methods in task 2, while there is no winning in task 3. [For the full proceedings, see ED615472.]

With the increased number of MOOC offerings, it is unclear how these courses are related. Previous work has focused on capturing the prerequisite relationships between courses, lectures, and concepts. However, it is also essential to model the content structure of MOOC courses. Constructing a precedence graph that models the similarities and variations of learning paths followed by similar MOOCs would help both students and instructors. Students can personalize their learning by choosing the desired learning path and lectures across several courses guided by the precedence graph. Similarly, by examining the precedence graph, instructors can 1) identify knowledge gaps in their MOOC offerings, and 2) find alternative course plans. In this paper, we propose an unsupervised approach to build the precedence graph of similar MOOCs, where nodes are clusters of lectures with similar content, and edges depict alternative precedence relationships. Our approach to cluster similar lectures based on PCK-Means clustering algorithm that incorporates pairwise constraints: Must-Link and Cannot-Link with the standard K-Means algorithm. To build the precedence graph, we link the clusters according to the precedence relations mined from current MOOCs. Experiments over real-world MOOC data show that PCK-Means with our proposed pairwise constraints outperform the K-Means algorithm in both Adjusted Mutual Information (AMI) and Fowlkes-Mallows scores (FMI). [For the full proceedings, see ED607784.]

This paper addresses the question of identifying a concept dependency graph for a MOOC through unsupervised analysis of lecture transcripts. The problem is important: extracting a concept graph is the first step in helping students with varying preparation to understand course material. The problem is challenging: instructors are unaware of the student preparation diversity and may be unable to identify the right resolution of the concepts, necessitating costly updates; inferring concepts from groups suffers from polysemy; the temporal order of concepts depends on the concepts in question. We propose innovative unsupervised methods to discover a directed concept dependency within and between lectures. Our main technical innovation lies in exploiting the temporal ordering amongst concepts to discover the graph. We propose two measures--the Bridge Ensemble Measure and the Global Direction Measure--to infer the existence and the direction of the dependency relations between concepts. The bridge ensemble measure identifies concept overlap between lectures, determines concept co-occurrence within short windows, and the lecture where concepts occur first. The global direction measure incorporates time directly by analyzing the concept time ordering both globally and within lectures. Experiments over real-world MOOC data show that our method outperforms the baseline in both AUC and precision/recall curves. [For the full proceedings, see ED593090.]