A LEARNER INTERACTION STUDY OF DIFFERENT ACHIEVEMENT GROUPS IN MPOCS WITH LEARNING ANALYTICS TECHNIQUES

The purpose of this study was to conduct data-driven research by employing learning analytics methodology and Big Data in learning management systems (LMSs), and then to identify and compare learners’ interaction patterns in different achievement groups through different course processes in Massive Private Online Courses (MPOCs). Learner interaction is the foundation of a successful online learning experience. However, the uncertainties about the temporal and sequential patterns of online interaction and the lack of knowledge about using dynamic interaction traces in LMSs have prevented research on ways to improve interactive qualities and learning effectiveness in online learning. Also, most research focuses on the most popular online learning organization form, Massive Open Online Courses (MOOCs), and little online learning research has been conducted to investigate learners’ interaction behaviors in another important online learning organization form: MPOCs. To fill these needs, the study pays attention to investigate the frequent and effective interaction patterns in different achievement groups as well as in different course processes, and attaches importance to LMS trace data (log data) in better serving learners and instructors in online learning. Further, the learning analytics methodology and techniques are introduced here into online interaction research. I assume that learners with different achievements express different interaction characteristics. Therefore, the hypotheses in this study are: 1) the interaction activity patterns of the high-achievement group and the low-achievement group are different; 2) in both groups, interaction activity patterns evolve through different course processes (such as the learning process and the exam process). The final purpose is to find interaction activity patterns that characterize the different achievement groups in specific MPOCs courses. Some learning analytics approaches, including Hidden Markov models (HMMs) and other related measures, are taken into account to identify frequently occurring interaction activity sequence patterns of High/Low achievement groups in the Learning/Exam processes under MPOCs settings. The results demonstrate that High-achievement learners especially focused on content learning, assignments, and quizzes to consolidate their knowledge construction in both Learning and Exam processes, while Low-achievement learners significantly did not perform the same. Further, High-achievement learners adjusted their learning strategies based on the goals of different course processes; Low-achievement learners were inactive in the learning process and opportunistic in the exam process. In addition, despite achievements or course processes, all learners were most interested in checking their performance statements, but they engaged little in forum discussion and group learning. In sum, the comparative analysis implies that certain interaction patterns may distinguish the High-achievement learners from the Low-achievement ones, and learners change their patterns more or less based on different course processes. This study provides an attempt to conduct learner interaction research by employing learning analytics techniques. In the short term, the results will give in-depth knowledge of the dynamic interaction patterns of MPOCs learners. In the long term, the results will help learners to gain insight into and evaluate their learning, help instructors identify at-risk learners and adjust instructional strategies, help developers and administrators to build recommendation systems based on objective and comprehensive information, all of which in turn will help to improve the achievements of all learner groups in specific MPOC courses

A Bayesian Approach for Structural Learning with Hidden Markov Models

Hidden Markov Models(HMM) have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K-means clustering method. Experimental results with artificially generated data show the effectiveness of the approach