Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study

Recommender systems require input information in order to properly operate and deliver content or behaviour suggestions to end users. eLearning scenarios are no exception. Users are current students and recommendations can be built upon paths (both formal and informal), relationships, behaviours, friends, followers, actions, grades, tutor interaction, etc. A recommender system must somehow retrieve, categorize and work with all these details. There are several ways to do so: from raw and inelegant database access to more curated web APIs or even via HTML scrapping. New server-centric user-action logging and monitoring standard technologies have been presented in past years by several groups, organizations and standard bodies. The Experience API (xAPI), detailed in this article, is one of these. In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems. We next review standardization efforts in this area; finally, we focus on xAPI and the potential interaction with the LIME model, which will be also summarized below

A Mixed Methods Study Of The Implementation Of Collaborative Technology Tools For Enhancing Collaboration And Student Engagement In Online Learning: Faculty Experiences And Student Perspectives

The appropriate implementation of collaborative technology tools in online courses leads to a culture of social learning where technology empowers students to take central roles in their learning. Yet, critical questions still exist about how faculty design, develop, implement collaborative eLearning activities using technology tools that support collaboration and student engagement in online courses, and what perspectives students have toward their experiences while participating in these activities. The purpose of the study is to explore the experiences of faculty members implementing collaborative technology tools in online courses to support collaboration and student engagement, in addition, to obtain the perspectives of students toward their experiences while participating in these activities. The study attempts to better understand the potential and use of technology for enhancing collaboration and student engagement in online settings and the factors that influence the selection of collaborative technology tools for incorporating collaborative eLearning activities in online courses. An explanatory sequential mixed methods approach was utilized to collect data from a total of 210 faculty and student participants who met the participation criteria and volunteered to participate in the study at a large Midwestern state university. Out of the 210 participants, 29 faculty members and 181 students were surveyed, and after a review of the results, follow-up interviews were conducted with four faculty members and two students. The findings of this study confirmed that collaborative technology tools have the potential to create a virtual collaborative environment that enables instructors to establish a learning community within online courses where students can synchronously or asynchronously work together toward a common task, in which each student adds to an emerging pool of knowledge of the group. This study provides evidence that the use of collaborative technology tools positively affects students’ experiences with collaborative eLearning activities in online learning. The instructor\u27s ability to successfully select and implement collaborative technology tools that effectively support collaborative eLearning and student engagement in online courses is a primary concern. This concern raises the demand for online instructors who are well-prepared and fully-supported to integrate collaborative technology tools into online settings and design eLearning activities that engage students and foster interaction and collaboration. Possible implications of the study and practical recommendations drawn from the findings of the study for professional and meaningful practice are discussed

MOOClm: Learner Modelling for MOOCs

Massively Open Online Learning systems, or MOOCs, generate enormous quantities of learning data. Analysis of this data has considerable potential benefits for learners, educators, teaching administrators and educational researchers. How to realise this potential is still an open question. This thesis explores use of such data to create a rich Open Learner Model (OLM). The OLM is designed to take account of the restrictions and goals of lifelong learner model usage. Towards this end, we structure the learner model around a standard curriculum-based ontology. Since such a learner model may be very large, we integrate a visualisation based on a highly scalable circular treemap representation. The visualisation allows the student to either drill down further into increasingly detailed views of the learner model, or filter the model down to a smaller, selected subset. We introduce the notion of a set of Reference learner models, such as an ideal student, a typical student, or a selected set of learning objectives within the curriculum. Introducing these provides a foundation for a learner to make a meaningful evaluation of their own model by comparing against a reference model. To validate the work, we created MOOClm to implement this framework, then used this in the context of a Small Private Online Course (SPOC) run at the University of Sydney. We also report a qualitative usability study to gain insights into the ways a learner can make use of the OLM. Our contribution is the design and validation of MOOClm, a framework that harnesses MOOC data to create a learner model with an OLM interface for student and educator usage