Design and Implementation of Gamification in a Social e-Learning Platform for Increasing Learner Engagement

Description

During the last couple of decades, technological evolution has dramatically changed humans’ lives. In the era of the Internet, many aspects of our lives take place online. Social
networks like Facebook replicates communities in a massive online environment. Here, people socialize all around the world, spending a significant amount of their time on the
web. In education, this provides plenty of opportunities to support learners. Indeed, the Internet offers billions of resources covering any kind of topic. All of this assumes
paramount importance, as we are facing a global pandemic due to the Coronavirus outbreak. Students are forced to learn online. Professors need to effectively support them
at a distance. Several solutions have been proposed. MOOCs, note-taking applications, Learning Management Systems, online channels, and more. However, people still experience severe difficulties while learning online. Students spend more time searching for material instead of learning. As social networks achieved resounding success in numerous fields, they are getting a lot of attention from the learning community. However, due to the lack of a consolidated platform, social learning offers a lot of space to work on. This thesis operates in a novel context provided by Fullbrain: a crowd-sourced social e-learning platform. To address the aforementioned problems, Fullbrain offers dedicated spaces for courses and concepts, where students collaborate, get help, and find personalized learning material. Like the large majority of social platforms, Fullbrain suffers from under contribution. Even in successful products like Twitter, barely 1% of the users produce half of the content. Upon this problem, this thesis aims to answer the following question: How can we increase learner engagement and contribution in the Fullbrain social e-learning platform? Several studies successfully exploited learning analytics to enhance education. In social media, a lot of attention has been directed towards social network analysis. Recently, gamification showed positive results in different domains. Despite a lot of studies have been carried out, little research has been done in the specif context of a social e-learning platform. To the best of my knowledge, no research has combined learning analytics, social learning analytics, and gamification for increasing user engagement and contribution. To address the research question, this thesis proposes three elements: the includer suggestions (an user recommendation system), a leaderboard, and the action recommender (an action recommendation system). The includer suggestions exploits social learning analytics to increase engagement and interactions in new users. The leaderboard and the action recommender combine learning analytics, social network analytics, and gamification to increase user engagement and contribution, by providing learners elements of reflection, motivation, socialization, self-assessment, and competition. The leaderboard proposed a new hybrid design, mixing absolute and relative approaches. This varies according to the context
in which the leaderboard is presented. Upon the leaderboard, the action recommender suggests personalized goals to users, leveraging friendships, and learner communities to
achieve a higher impact. All three elements were implemented in the Fullbrain platform. Due to the project limitations, the effectiveness of the proposed solutions could not be
exhaustively validated. However, a longitudinal validation showed promising results, suggesting that exploiting learning analytics and social network analytics through gamified
elements can positively affect learner engagement and contributions in a social learning platform.

Period	15 Jun 2020 → 30 Nov 2020
Examinee	Mirko Biasini
Degree of Recognition	International