Student-Based Gamification Framework for Online Courses

Calle-Archila, Carlos Ricardo; Mariño, Olga

doi:10.1007/978-3-319-66562-7_29

Cited by 7 publications

(10 citation statements)

References 21 publications

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“…Gametize [24] MyMOOCSpace [25], [26], [27] SBGF [28] iMOOX [29] OpenHPI [30], [31] MEdit4CEP-Gam [32] INDIeAuthor [33], [34] OneUp [23], [35], [19], [15] TABLE 2. Technological systems supporting the use of gamification in MOOCs.…”

Section: System Referencesmentioning

confidence: 99%

GamiTool: Supporting Instructors in the Gamification of MOOCs

et al. 2022

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Reward-based gamification strategies are proposed as a promising technique to increase student engagement in Massive Open Online Courses (MOOCs), following its success in other smallscale educational settings. However, these strategies imply a number of orchestration tasks (e.g., design, management) that are usually carried out by instructors, and which may hinder their use and adoption. Furthermore, some MOOC distinctive features (e.g., scale, 24/7 availability, etc.) have considerable implications on how these gamification strategies are orchestrated, resulting in an unmanageable instructors' workload in cases of manual operation. Therefore, an eventual adoption of gamification in MOOCs calls for automatic systems capable of decreasing the additional workload of instructors. The limitations identified in the current solutions (e.g., non-usable graphical interfaces, inflexible gamification designs) led us to propose and develop a new gamification system named GamiTool. An evaluation with 19 MOOC instructors and gamification designers showed the high design expressiveness, usability and potential for adoption of GamiTool. Hence, GamiTool can be used by instructors to improve students' engagement, and also, by researchers to keep understanding the effects of gamification in MOOC settings.

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Section: System Referencesmentioning

confidence: 99%

GamiTool: Supporting Instructors in the Gamification of MOOCs

et al. 2022

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“…and alignment of reward-based strategies with learning contents (e.g., [280,226]); and, do not tackle the issue of how costly it is for practitioners that are not experts in ICT, to put these strategies in practice in authentic MOOC settings (e.g., [40,217]). These current limitations can have a negative effect in the attainment of the expected gamification and pedagogical goals, in the affordability of using these practices, and in the end, in the adoption of gamification strategies in MOOC environments.…”

Section: Motivationmentioning

confidence: 99%

“…The MOOC context (e.g., topic, activities) and the characteristics of its cohort of participants are two important variables in the gamification design that potentially impact the effects of gamification on learners [111,253]. Since there exists a myriad of MOOCs about hundreds of topics, each of them implementing different types of activities and resources, and targeting different learning populations (e.g., software programmers, inservice teachers), systems supporting the design of reward-based strategies in MOOCs should permit a high flexibility in the design of gamified MOOCs [9,40,7]. This flexibility should allow practitioners achieve the gamification expected goals (e.g., promote student positive interdependence); align the gamification purposes with the course pedagogical contents, tools and goals (e.g., collaborative learning); and, avoid negative counter effects on students (e.g., off-task behavior).…”

Section: Design Expressivenessmentioning

confidence: 99%

“…Accordingly, 8 publications ranging from 2014 to 2018 and describing 5 different gamification systems intended for MOOC environments were identified: iMOOX [280], OpenHPI [268,267], SBGF [40], MyMOOCSpace [227,228,226], and Gametize-based system [21].…”

Section: Systemsmentioning

confidence: 99%

“…This model should feature a high design expressiveness, allowing the representation of as many as possible reward-related decisions without constraining the gamification purposes and course pedagogical goals. Limitations in the design expressiveness might hinder the use of reward-based strategies and their alignment with the pedagogical goals of the course, thus precluding their benefits and increasing the possibility of producing negative counter effects (e.g., off-task behavior) [9,40,7]. For instance, let us imagine a practitioner that wants to promote collaborative learning in groups with gamification elements.…”

Section: Introductionmentioning

confidence: 99%

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Supporting Practitioners in the Gamification of MOOCs through Reward-Based Strategies

Arranz¹

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Massive Open Online Courses (MOOCs) have been established as a complement or alternative to other more traditional forms of teaching and learning (e.g., face-to-face, blended learning). Despite their relevant benefits (e.g., open access to education from prestigious universities), student disengagement has been identified as an important shortcoming, contributing to high drop out rates. In order to overcome this problem, rewardbased gamification has been proposed as a promising strategy to increase student engagement in MOOCs, following its success in other small-scale educational contexts. However, the addition of gamification strategies implies a number of orchestration tasks (e.g., design, instantiation, management) that have to be carried by course practitioners (instructional designers, instructors, teacher assistants, etc.). Given this context, this dissertation aims to support MOOC practitioners in the design, instantiation and management of reward-based strategies in MOOC environments to promote students' engagement. To this end, this dissertation proposes the attainment of three goals by following the System Design Research Methodology.The first goal deals with understanding whether reward-based strategies provide fruitful effects on student engagement in MOOCs. The distinctive features of MOOCs (e.g., massive number of participants, participants' background heterogeneity, asynchronous interaction) might compromise the benefits observed in small-scale educational environments. A systematic literature review performed within the context of this dissertation, revealed a lack of empirical studies performed in real MOOC environments, thus hindering the understanding of how these strategies affect student engagement. In this sense, three empirical studies were carried out in the context of this dissertation. The three studies involved MOOCs that incorporated reward-based strategies, helping understand their effects, and gaining insights about potential design guidelines that might eventually be used by practitioners in the design of future MOOCs.The second goal refers to the need of providing practitioners with computer-interpretable models to represent MOOC learning designs, incorporating reward-based strategies, thus supporting their automatic instantiation and management (e.g., reward-issuing procedure). A feature analysis of MOOC platforms and gamification systems identified their limited support regarding the representation of reward-based strategies in MOOC platforms' native tools. Given this context, this dissertation presents a data model (GamiTool-DM) that supports the computer-interpretable representation of reward-based strategies in MOOCs with a fine-grained level of detail, thus allowing to align practitioners' gamification purposes with the course pedagogical goals.The aforementioned feature analysis also revealed that current MOOC platforms and gamification systems present some limitations regarding the cognitive and timely affordable orchestration of these strategies (e.g., usable authoring to...

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Application of artificial intelligence in adaptation of gamification in education: A literature review

Suresh Babu,

Dhakshina Moorthy

2023

Comp Applic In Engineering

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The education system has been changing continuously with several new strategies for effective student learning. E‐learning has provided the opportunity for students to learn from anywhere and from anyone. To motivate the learners and improve their performance in e‐learning courses, different pedagogical activities have been practiced among which gamification has a good edge. Gamification is one of the upcoming fields in education for getting students motivated and engaged through game‐like activities with various gamification elements in nongame settings. Gamification can be further improved by dynamically adapting the mechanics and dynamics of gamification to a variety of factors such as the personality, needs, values, performance, and motivations of learners. The advancement of artificial intelligence (AI) enables the development of dynamically adapting gamification environments with established AI techniques. The application of AI in adaptive gamification needs to be further explored to discover new approaches. This study provides a comprehensive report of adaptive gamification from the perspective of the existing studies, which map different gamification elements, user profile elements, and intelligent techniques through a systematic literature analysis. Research questions have been formulated and the findings have been presented by exploring 29 focused studies from a selected pool of 622 research articles. The findings have identified the commonly used gamification elements and presented the methods of incorporating them with various AI techniques based on different user profile elements.

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Student-Based Gamification Framework for Online Courses

Cited by 7 publications

References 21 publications

GamiTool: Supporting Instructors in the Gamification of MOOCs

GamiTool: Supporting Instructors in the Gamification of MOOCs

Supporting Practitioners in the Gamification of MOOCs through Reward-Based Strategies

Application of artificial intelligence in adaptation of gamification in education: A literature review

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