Examining Individuals’ Utilization of SPOC: Extending the Task-Technology Fit Model with Online and Offline Perspective

Small Private Online Course (SPOC) platform enables individuals to carry out their learning tasks both online and offline. In order to understand individuals’ utilization of SPOC, this study develops a research model to examine the joint influences of three types of perceived fit manifested in perceived technology-task fit (TTF), perceived individual-technology fit (ITF) and perceived online-offline fit (OOF). A survey was conducted in a famous university of China and 371 data were collected from students who selected courses on the SPOC platform. Structural equation modelling method was used to examine the research model. The empirical results suggest that ITF is the most significant antecedent of individual performance expectancy, followed by OOF and TTF. Moreover, individual performance expectancy has a positive influence on user satisfaction and individuals’ continuance intention in the SPOC platform. A post-hoc analysis further indicates that student’s GPA positively moderates the relationship between online participation behavior and course performance. This study extends the traditional perceived fit framework by introducing perceived online-offline fit, and uncovers the antecedents and outcomes of individuals’ utilization in the emerging research context of SPOC

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

European Distance and E-Learning Network (EDEN). Conference Proceedings

Erasmus+ Programme of the European UnionThe powerful combination of the information age and the consequent disruption caused by these unstable environments provides the impetus to look afresh and identify new models and approaches for education (e.g. OERs, MOOCs, PLEs, Learning Analytics etc.). For learners this has taken a fantastic leap into aggregating, curating and co-curating and co-producing outside the boundaries of formal learning environments – the networked learner is sharing voluntarily and for free, spontaneously with billions of people.Supported by Erasmus+ Programme of the European Unioninfo:eu-repo/semantics/publishedVersio

The impact of emotions on student participation in an assessed, online, collaborative activity

There is growing recognition of the importance of emotions in academic online learning contexts. However, there is still little known about the role of emotions in social and collaborative online learning settings, especially the relationship between emotions and student participation. To explore this relationship, this study used a prospective longitudinal research design to follow 46 distance learning students throughout a 3-week assessed, online, collaborative activity. This approach allowed the fluctuating and dynamic aspects of emotions to be explored as well as the relationship between emotions and student participation in the collaborative activity. Self-report data were gathered using a semistructured online diary at five time points throughout the task (once at the start of the collaborative activity, three times during the activity, and the final entry after the activity had finished). Findings revealed that learners generally perceived pleasant emotions (such as relief, satisfaction and enjoyment) to have positive impacts, or no impact, on participation, whereas unpleasant emotions (such as anxiety, frustration, and disappointment) were generally perceived to have negative impacts, or no impact, on participation. Interestingly, however, anxiety, and to a smaller extent frustration, were perceived by a number of students to have positive impacts during the activity. To conclude this paper, implications for educators are highlighted