Adaptive and cooperative model of knowledge management in MOOCs

One of the characteristics of Massive Open Online Courses (MOOC) is the heterogeneity of their participants’ profiles and, for the most traditional MOOC model, this is an important cause of the low completion rate. The MOOC model presents two apparent antagonistic concepts, globalization and diversity. MOOCs represent globalization (participants have to be adapted to the course) and their participants represent diversity. The authors of this paper argue that both concepts complement each other; that is, a MOOC can adapt the contents and navigation to the diversity of participants; and in turn the participants themselves can increase and improve the contents of the MOOC, through heterogeneous cooperation, to encourage massive learning. To proof it, this paper presents a new model, called ahMOOC, combining the hybrid-MOOC (hMOOC) and the adaptive MOOC (aMOOC). The hMOOC allows integrating characteristics of xMOOCs (based on formal e-training) with cMOOCs (based on informal and cooperative e-training). The aMOOC offers different learning strategies adapted to different learning objectives, profiles, learning styles, etc. of participants. The ahMOOCs continues having a lower dropout rate (such as hMOOC) than the traditional MOOCs. The qualitative analysis show the capacity of participants, with heterogeneous profiles, to create, in a cooperative and massive way, useful knowledge to improve the course and, later, to apply it in their specific work context. The study also shows that participants have a good perception on the capabilities of the ahMOOC to adapt the learning process to their profiles and preferences

Methodological Approach and Technological Framework to Break the Current Limitations of MOOC Model

[EN]A methodological approach and technological framework are proposed to improve learning outcomes in Massive Open Online Courses (MOOCs), taking into account the distinguishing features of this kind of massive courses over traditional online courses. The proposed methodology integrates the learning strategies of xMOOCs and cMOOCs with adaptivity and knowledge management capabilities. In order to test the learning results of the methodology and the need of supporting technological framework for it, a MOOC was made based on the methodological proposal and using a MOOC platform called MiríadaX. The quantitative results have improved considerably the MOOC completion rate (compared to the average of the rest of MOOC MiríadaX) and the qualitative results show a great satisfaction with the learning outcomes of the learners. However, the technological environment did not allow us develop all the methodological capabilities and it was one of the main concerns of the MOOC attendances. Therefore, from the analysis of collected data and considering the limitations of current MOOC technology platforms, a technological framework has been designed. It may incorporate the proposed methodology in an efficient and effective way. Based on this proposed technological framework, a MOOC platform has been developed and delivered, used by three Spanish Universities to offer MOOCs. This new platform and the supported technological framework have been tested with a first pilot with promising result

Evaluating Learner Engagement with Gamification in Online Courses

Several reasons underlie the low retention rates in MOOCs. These reasons can be analysed from different perspectives, either in terms of the course design or the enrolled students. On the student side, we find little social interaction, boredom, tiredness, and a lack of motivation and time. These challenges can be addressed by adaptive gamification that proposes the design of personalised, hedonic learning experiences. Studies to date have adopted either the one-fits-all approach or the adaptive approach. Nevertheless, the adaptive solutions have considered a static player profile throughout the entire experience. This paper presents the design and evaluation of a dynamic adaptive gamification approach which—based on students’ interactions with game elements and also their opinions about these elements—dynamically updates the students’ player profile to better figure out which game elements suit them. We evaluated the engagement of students with gamification elements by means of a course composed of a knowledge "pill" related to the topic of “recycling plastics from the sea”, offered through the nanoMOOCs learning platform. We propose metrics such as the mean number of interactions with the gamification dashboard, the time spent by participants with game elements, and the opinions of students about these elements to compare the Dynamic Adaptive Gamification (DynamicAG) and the Static Adaptive (StaticAG) approaches. An experimental study with 66 high school students showed significant differences between both approaches. Specifically, the DynamicAG group spent twice as much time with the Dashboard than the StaticAG group. Moreover, students in the DynamicAG group were more engaged with game elements (mean number of interactions = 12.13) than those in the StaticAG group (mean number of interactions = 3.21)

Beyond localization: making learning spaces accessible to all

When addressing localization within Translation Studies, we think of translating web content for a new linguistic and cultural reality. At times, localization is seen as making a text “adequate” to the new readership taking into account local sensitivities and requirements. When the addressees of a given web-based product have a disability, localization alone will not be sufficient to guarantee true access, for the needs and requirements will entail and also go beyond language and culture. This paper is highlighting the issues that need to be addressed to make online learning spaces accessible to all. The take on transadaptation, in the context of accessibility to educational environments, is holistic in nature, given that online learning platforms are required to be set up in line with WCAG directives from inception and that all uploaded content is made available in a variety of formats, among which are alternative texts, captions, audio description, sign language, just to name a few. Only in so doing with the service providers be guaranteeing that users with (sensory, cognitive or physical) impairment will benefit from such educational offers. To convey clearer understanding of the specificities of inclusive online education, two institutions from Portugal are presented in this paper showing the problems they faced and their efforts to make online learning spaces and MOOC accessible: the Polytechnic Institute of Leiria and the Open University. Examples are given from ongoing exercises, and reflections are shared on the cycles of improvement that are necessary to ensure the highest possible standards of inclusion. Included is a comparative analysis of the needs and challenges expressed by students with either hearing or visual disabilities to ensure access to all types of online contents, including spontaneous content (e.g. messages in forums, collaborative online tools).info:eu-repo/semantics/publishedVersio

ECO D2.5 Learning analytics requirements and metrics report

In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided.Part of the work carried out has been funded with support from the European Commission, under the ICT Policy Support Programme, as part of the Competitiveness and Innovation Framework Programme (CIP) in the ECO project under grant agreement n° 21127

Elearning, Communication and Open-data: Massive Mobile, Ubiquitous and Open Learning

ABSTRACT: In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing