Lifelong Technology-Enhanced Learning

Kahn, Ken; Megasari, Rani; Piantari, Erna; Junaeti, Enjun

doi:10.1007/978-3-319-98572-5

Cited by 22 publications

(3 citation statements)

References 8 publications

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“…In this same area of data analysis, the team of Maldonado et al [46] investigated about predicting the success of learners in a MOOC through the identification of selfregulated learning sequence patterns; For this they used a sample of 2,035 students who performed a MOOC in Coursera's platform. Among the contributions found, the following are highlighted: the identification of self-regulated and self-reported learning strategies by students, as well as the identification of patterns of sequence of activities in MOOCs.…”

Section: Research Lines Associated With Data Analysismentioning

confidence: 99%

“…Figure 6 shows the Data Analysis Software used in the reviewed studies. The platforms that were most used to carry out research on self-regulated learning in MOOCs were: 1) Coursera (54%), used in the research by Alonso-Mencía et al, [33], [79], [80] 2) Edx (35%) in the works of Davis et al, [39], [81], [82]; 3) Moodle platform (5%) in the research by Sambe [19]. 4) Futurelearn and Open Edx (3%) in studies by Pérez-Álvarez et al, [83] and These findings are summarized in Figure 7.…”

Section: Strategy Description Autormentioning

confidence: 99%

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Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

et al. 2021

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Section: Research Lines Associated With Data Analysismentioning

confidence: 99%

Section: Strategy Description Autormentioning

confidence: 99%

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

et al. 2021

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“…In another study, Pierre Laforcade and Youness Laghouaouta proposed a technique, namely Model-Driven Engineering (MDE), to design game-based learning scenarios. MDE has a special ability to handle dynamic scenarios [23]. In 2019 they offered a concept in building adaptive scenarios for learning games.…”

Section: A Related Workmentioning

confidence: 99%

An Automatic Scenario Control in Serious Game to Visualize Tourism Destinations Recommendation

et al. 2021

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In the field of tourism, serious games are a pedagogical media application that helps players develop travel knowledge and expertise based on game content. A tourism serious game requires a scenario control system to visualize an attractive travel scenario. This paper proposes an Automatic Scenario Control in the serious game to visualize travel recommendation scenarios choice according to the player's expectations of potential tourism destinations criteria. There are two stages in system development, namely scenario design and scenario selection. In the scenario design stage, we use the Hierarchical Finite State Machine to translate challenge-based stories according to the type of attraction. While at the scenario selection stage, Dynamic Weight Topsis is a method for selecting one of the player's recommended scenarios. This study uses tourism destinations recommendations as to alternative variables, characteristics of tourism destinations as criteria, and players' expectations of tourism destinations' characteristics as weight criteria. In the implementation phase, the tourism serious game uses the content of tourism destinations in Mojokerto Indonesia. The test results show that Automatic Scenario Control generates a preference value for each alternative as a reference for choosing tourism destination scenarios for the player. Three things affect the scenario choice results, including the choice of month of tourist visits, player expectations of tourist destinations, and alternative input from the recommender system.

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Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning

Mejeh,

Sarbach,

Hascher

2024

Educ Inf Technol

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Lifelong learning is emerging as a key priority for promoting equity and sustainability in societies. Self-regulated learning (SRL) is a fundamental requirement for achieving successful lifelong learning, and digitization is increasingly influential in this regard. This mixed-methods study explores the degree to which adaptive learning technology (ALT) can assist university students in their SRL with timely and personalized support. Additionally, the study examines how students perceive this feedback and incorporate it into their learning behavior. Using hierarchical linear modeling, we investigated the development of SRL over a 9-week period. Semi-structured interviews were conducted with purposively selected learners, based on stimulated recalls. The quantitative results demonstrate positive development in certain components of SRL. Furthermore, the results indicate that metacognitive activity can be partially predicted by motivational and emotional states. The qualitative findings reveal that learners have varying perceptions of feedback received from ALT and integrate it into their learning behaviors based on their individual benefits. The results support the assumption that feedback provided through educational technology must be precisely tailored to the needs of learners, taking into account the dynamics of their individual learning processes. The study contributes to the ongoing discussion on the design of educational technology.

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Lifelong Technology-Enhanced Learning

Cited by 22 publications

References 8 publications

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

An Automatic Scenario Control in Serious Game to Visualize Tourism Destinations Recommendation

Effects of adaptive feedback through a digital tool – a mixed-methods study on the course of self-regulated learning

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