A gamification framework to enhance students’ intrinsic motivation on MOOC

Technological development supports the distribution of education to various parts of the world through online education. One of the learning media that supports the distribution of learning is the Massive Open Online Course (MOOC). However, MOOC has a low number of students who complete the course. Therefore, this research proposes a "gamification framework" through studies and various approaches in the field of games, intrinsic motivation elements, social learning, and interactive learning environments to overcome the low motivation of students. The proposed framework has been evaluated through validation by experts. The results found that the framework fulfilled the rules and suitability of the instruments and game elements used to increase the intrinsic motivation of students in online learning. Although there are some changes in the function and type of game elements used. For further research, the framework will be used as a guideline to build the Gamified MOOC Platform

To reward and beyond: Analyzing the effect of reward-basedstrategies in a MOOC

Producción CientíficaDespite the benefits of MOOCs (e.g., open access to education offered by prestigious universities), the low level of student engagement remains as an important issue causing massive dropouts in such courses. The use of reward-based gamification strategies is one approach to promote student engagement and prevent dropout. However, there is a lack of solid empirical studies analyzing the effects of rewards in MOOC environments. This paper reports a between-subjects design study conducted in a MOOC to analyze the effects of badges and redeemable rewards on student retention and engagement. Results show that the implemented reward strategies had not significant effect on student retention and behavioral engagement measured through the number of pageviews, task submissions, and student activity time. However, it was found that learners able to earn badges and redeemable rewards participated more in gamified tasks than those learners in the control group. Additionally, results reveal that the participants in the redeemable reward condition requested and earned earlier the rewards than those participants in the badge condition. The potential implications of these findings in the instructional design of future gamified MOOCs are also discussed.Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (project VA257P18)European Commission (project 588438-EPP-1-2017-1-EL- EPPKA2-KA

Creating Engaging Experiences in MOOCs through In-Course Redeemable Rewards

Gamification strategies have been proposed to mitigate student disengagement and dropouts in massive online environments, due to the positive results shown by these strategies at lower scales. Among various gamification strategies, redeemable rewards have been identified as an effective element to intrinsically motivate students and increase their engagement in educational settings, including MOOCs. Yet, effective design, implementation and enactment of this gamification strategy in MOOC contexts might face new challenges, given the unique characteristics of these learning settings such as massiveness. As an attempt to help teachers use redeemable rewards in MOOCs, this paper analyzes the characteristics of MOOCs that influence its integration and presents a proposal of a system supporting the design, implementation and enactment of such rewards. The envisioned system is illustrated by a scenario that describes the main features of this system for teachers and students.This research has been partially funded by the Spanish Ministry of Economy and Competitiveness, under project grants TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-R, and the Regional Goverment of Castilla y Leo ́n together with the European Regional Development Fund, under project grant VA082U16. The authors thank the rest of the GSIC-EMIC research team for their valuable ideas and support

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Producción CientíficaPeer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples.European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 793317)Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

Hezkuntza-Teknologia Unibertsitate Jardunaldiak : konpetentzia eta hiritartasun digitala gizarte eraldaketarako : Akten liburua, XXVI. edizioa, Donostian, 2018ko ekainak 27, 28 eta 29

415 p