Collaborative MOOC Content Design and Automatic Assessment Based on ODALA Approach

Collaborative MOOC Content Design and Automatic Assessment Based on ODALA Approach

Nacera Hammid (Mouloud Mammeri University, Department of Computer Sciences, Tizi-Ouzou, Algeria), Lynda Haddadi (Mouloud Mammeri University, Department of Computer Sciences, Tizi-Ouzou, Algeria) and Farida Bouarab-Dahmani (Mouloud Mammeri University, Department of Computer Sciences, Tizi-Ouzou, Algeria)
Copyright: © 2017 |Pages: 21
DOI: 10.4018/JITR.2017040102
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Abstract

Since the fall of 2011, the Massive Open Online Course (MOOC) phenomenon is still being qualified as the most attractive and discussed subject by educational communities and public. In the literature, there are many researches about this recent e-learning generation that vary as the goals vary from raising pedagogical issues to economics ones. Several case studies state that MOOCs are challenging the use of technologies to enhance learning; others think that MOOCs can induce to disruptive in education and educational institutions. In this paper, we propose an instructional design for a kind of MOOC platforms where mainly the use of disciplines specifications and automated evaluation of MOOC learners are possible to settle the source of these problems. Our proposition is based on ODALA (Ontology-Driven Auto-evaluation Learning Approach) principles and on the disciplines' knowledge capitalization using a meta-model represented as domain ontology for disciplines modeling inspired by this approach.
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Introduction

The term MOOC (Massive Open Online Course) was coined in 2008 by Dave Cormier. The first MOOC ‘Connectivism and Connective Knowledge’ (CCK08) was led by George Siemens of Athabasca University and Stephen Downs of National Research Council (Cormier, 2008). Massive course refers to the important number of the participants in this course. Open course means that everyone from anywhere can access this course without fees. Online course refers to the course that is distributed online. Many types of MOOCs are represented in literature (George,2012; Lane, 2012; Clark, 2013; Gilliot, 2013; Rosselle, 2014; Schoenack, 2013), we retain two main types:

  • cMOOC (Connectivist MOOCs): That emphasizes the Connectivism learning theory, where courses are organized by group having specific focus, encouraging participants to collaborate, share and contribute in building creative knowledge.

  • xMOOC (extended MOOCs): That refers mostly to MOOCs led by universities. With this kind of systems, courses look like traditional courses presented as videos lectures and online interactive exercises as tests and quizzes with possible home work.

This distinction between these two types is viewed by George Siemens (Cisel, 2013) as two types addressing two different needs of knowledge respectively the creative knowledge and the basic knowledge. Our proposition doesn’t focus on a specific kind of MOOC but gives a model for structuring the course content and suggests an effective way of evaluating the participants. In other words, we suggest a MOOC framework where the engineering pedagogy is based on ODALA approach (Ontology-Driven Auto-evaluation Learning Approach). This last supports the domain or discipline representation, learners’ profiling and evaluation (see Bouarab-Dahmani, Si-Mohammed, Comparot, & Charrel, 2011) for more details). Moreover, we propose a disciplinary knowledge capitalization process to get a consensus in a given context (national, regional, all the world, etc.) about the discipline which is the subject of the MOOC that we aim to build. This process is seen as a collaborative tool (that can be itself a cMOOC) integrated or not in the MOOC platform. We also suggest improvements in the existing platforms by adding functionalities inspired by ODALA approach to give the developers the opportunity of integrating more automated tasks to guide, evaluate and track the learners.

In what follows, we first give the background of our proposition exposing the existing MOOC platforms, their functionalities, listing the most challenges of MOOCs and introducing the ODALA approach where we present details about the domain specification and the recommended automated evaluation process. In the next point, we explain our proposition of a MOOC system based on ODALA approach and composed of a MOOC platform and a MOOC engineering tool. Before concluding, we expose the feasibility study conducted on the system.

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