74 research outputs found

    Increasing MOOC completion rates through social interactions: a recommendation system

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    International audienceE-learning research shows students who interact with their peers are less likely to drop out from a course, but is this applicable to MOOCs? This paper examines MOOC attrition issues and how encouraging social interactions can address them: using data from 4 sessions of the GdP MOOC, a popular Project Management MOOC, we confirm that students displaying a high level of social interaction succeed more than those who don't. We successively explore two approaches fostering social interactions: 1) in MOOC GdP5, we give access to private group forums, testing various group types and sizes, 2) in MOOC GdP6, we implement a recommendation system, suggesting relevant chat contacts using demographic and progression criteria. This papers presents our preliminary findings

    Does a Peer Recommender Foster Students' Engagement in MOOCs?

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    International audienceOverall the social capital of MOOCs is under-exploited. For most students in MOOCs, autonomous learning often means learning alone. Students interested in adding a social dimension to their learning can browse discussion threads, join social medias and may decide to message other students… but usually in a random way. This common isolation might be a contributing factor on student attrition rate and on their general learning experience. To foster learners' persistence in MOOCs, we propose to enhance the MOOC experience with a recommender which provides each student with an individual list of rich-potential contacts, created in real-time on the basis of their own profile and activities. This paper describes a controlled study conducted from Sept. to Nov. 2015 during a MOOC on Project Management. A recommender panel was integrated to the users' interface and allowed to manage contacts, send them an instant message or consult their profile. The population (N = 8,673) was randomly split into 2 parts: a control group, without any recommendations, and an experimental group in which students could choose to activate and use the recommender. After having demonstrated that these populations were similar up to the activation of the recommender, we evaluate the effect of the recommender on the basis of four pillars of learners' persistence: attendance, completion, success and participation. Results suggest that the recommender improved all these four factors: students were much more likely to persist and engage in the MOOC if they received recommendations than if they did not

    The use of text and process mining techniques to study the impact of feedback on students’ writing processes

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    Understanding the impact of feedback in complex learning activities, such as writing, is challenging. We contribute a combination of writing environments and data and process mining tools that can provide new ways of measuring this impact. We use the tools in a field experiment in an engineering course (N=45). Responses (timing, amount and types of text changes) were examined using log data and process mining techniques. Two experimental conditions were used: reflective followed by directive feedback (A) and vice-versa (B). We found that both forms of feedback were read multiple times. Students required longer times to respond to reflective, compared to directive, feedback. The type of feedback, however, made little difference to the types of revisions that students performed. Overall, our findings point to the difficulty of encouraging students to reconsider and revise what they have already written
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