Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators

Ruipérez‐Valiente, José A.; Muñoz‐Merino, Pedro J.; Kloos, Carlos Delgado

doi:10.1111/exsy.12298

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…1) Learning Gains: Student's learning with a computerbased learning approach is usually evaluated using the relationship between a pre-test and a post-test scores to determine the learning gains and normalized learning gains [25]. The tests given prior to the provision of computer-based instructions or interventions are pre-tests, while the tests administered after the intervention are post-test [26].…”

Section: B Evaluation Methodsmentioning

confidence: 99%

The Effects of Adaptive Feedback on Student’s Learning Gains

Bimba¹,

Idris²,

Al-Hunaiyyan³

et al. 2021

IJACSA

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There is an increase in the implementation of adaptive feedback models, which focus on the relationship between adaptive feedback and learning gains. These literatures suggest that the complex relationship between feedback, task complexity, pedagogical principles and student's characteristics affect the significance of feedback effects. However, current studies have shown insufficient research on the effect of adaptive feedback characteristics on student's learning gains. Thus, there is a need to investigate the effect of multiple adaptive feedback characteristics on student's learning gains. The adaptive feedback model proposed supports the retrieval of appropriate feedback for students based on established weights between related concepts. In comparing three experimental groups, students who were provided with adaptive feedback showed learning gains and normalized learning gains of 0.87 and 0.05 over the normal feedback group, with 0.97 and 0.07 over the non-feedback group. This research yielded better outcomes than previous similar studies.

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Section: B Evaluation Methodsmentioning

confidence: 99%

The Effects of Adaptive Feedback on Student’s Learning Gains

Bimba¹,

Idris²,

Al-Hunaiyyan³

et al. 2021

IJACSA

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show abstract

“…As a part of future work, we would like to better connect in-game metrics with player outcomes such as learning gains and self-efficacy. Previous work in the area [54] has used behavioral metrics to predict learning gains and investigate the individual effect of such metrics in how much a student learns; we would like to replicate those analyses in a Radix pilot study by using the pre-and post-tests that many school students completed. We would also like to investigate the impact of providing this information to teachers and students, particularly to improve students' awareness of their own learning processes and teachers' classroom practices.…”

Section: Conclusion Limitations and Future Workmentioning

confidence: 99%

Patterns of Engagement in an Educational Massively Multiplayer Online Game: A Multidimensional View

Ruipérez‐Valiente¹,

Gaydos²,

Rosenheck

et al. 2020

IEEE Trans. Learning Technol.

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Learning games have great potential to become an integral part of new classrooms of the future. One of the key reported benefits is the capacity to keep students deeply engaged during their learning process. Therefore, it is necessary to develop models that can measure quantitatively how learners are engaging with learning games to inform game designers and educators, and to find ways to maximize learner engagement. In this work, we present our proposal to multidimensionally measure engagement in a learning game over four dimensions: general activity, social, exploration, and quests. We apply metrics from these dimensions to data from The Radix Endeavor, an inquiry-based online game for STEM learning that has been tested in K-12 classrooms as part of a pilot study across numerous schools. Based on these dimensions, we apply clustering and report four different engagement profiles that we define as: "integrally engaged," "lone achiever," "social explorer," and "non-engaged." We also use three variables (account type, class grade, and gender) to perform a cross-sectional analysis finding interesting, statistically significant differences in engagement. For example, in-school students and accounts registered to males engaged socially much more than out-of-school learners or accounts registered to females, and that older students have better performance metrics than younger ones.

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“…Yu [30] used combined linear regression and deep neural network (DNN) to predict the final score of a computer science course. Moreover, Ruipérez-Valiente et al [31] predicted learning gains in a preparatory course for freshmen students. This article presents a similar kind of study, although the variables related to learners' interactions and context (e.g., course duration and objective, pedagogy, etc.)…”

Section: Prediction In Educationmentioning

confidence: 99%

Generalizing Predictive Models of Admission Test Success Based on Online Interactions

et al. 2019

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To start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners’ interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models.

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Improving the prediction of learning outcomes in educational platforms including higher level interaction indicators

Cited by 12 publications

References 30 publications

The Effects of Adaptive Feedback on Student’s Learning Gains

The Effects of Adaptive Feedback on Student’s Learning Gains

Patterns of Engagement in an Educational Massively Multiplayer Online Game: A Multidimensional View

Generalizing Predictive Models of Admission Test Success Based on Online Interactions

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