Assessing the Effectiveness of Student Advice Recommender Agent (SARA): the Case of Automated Personalized Feedback

Mousavi, Amin; Schmidt, Matthew; Squires, Vicki; Wilson, Ken

doi:10.1007/s40593-020-00210-6

Cited by 19 publications

(11 citation statements)

References 33 publications

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“…Living Letters An online ITS, which offers personalized instruction, hints, and corrections to focus students on solving target problems Kegel and Bus ( 2012 ) 8. SARA An ITS which can provide automated and personalized feedback to students Mousavi et al ( 2021 ) 9. FB-TS An ITS developed based on Fuzzy logic and Bayesian network, which can provide adaptive support to students Eryilmaz and Adabashi ( 2020 ) 10.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, natural experiment (5%), longitudinal study (10%), and Randomized Alternative-Treatment Design (2.5%) were rarely used. Finally, only 13.5% of the studies (n = 6) used the matching technique to formulate comparable control groups (Cung et al, 2019 ; Hickey et al, 2020 ; Mousavi et al, 2021 ; Pane et al ( 2014 ); Spichtig et al, 2019 ; Troussas et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…Finally, only 13.5% of the studies (n = 6) used the matching technique to formulate comparable control groups (Cung et al, 2019 ; Hickey et al, 2020 ; Mousavi et al, 2021 ; Pane et al ( 2014 ); Spichtig et al, 2019 ; Troussas et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

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Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

et al. 2023

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Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs work in real and natural educational contexts and the associated challenges of ITS application and evaluation. Through a systematic literature review method, this study analyzed 40 qualified studies that applied social experiment methods to examine the effectiveness of ITS during 2011–2022. The obtained results highlighted a complicated landscape regarding the effectiveness of ITS in real educational contexts. Specifically, there was an “intelligent” regional gap regarding the distribution of countries where ITS studies using social experiment methods were conducted. Compared to learning performance, relatively less attention was paid to investigating the impact of ITS on non-cognitive factors, process-oriented factors, and social outcomes, calling for more research in this regard. Considering the complexities and challenges existing in real educational fields, there was a lack of scientific rigor in terms of experimental design and data analysis in some of the studies. Based on these findings, suggestions for future study and implications were proposed.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

et al. 2023

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“…However, propensity score matching has been associated with shortcomings (King & Nielsen, 2019), while other matching procedures like nearest neighbour matching (using Mahanalobis Distance) and (coarsened) exact matching having been shown to avoid these shortcomings (King et al, 2011). Indeed, a comparison of matching methods in the context of assessing the efficacy of automated feedback showed that nearest neighbour matching using Mahanalobis Distance was the most suitable approach and outperformed the more common Propensity Score approach (Mousavi et al, 2021).…”

Section: Statistical Controlmentioning

confidence: 99%

Causal Inference and Bias in Learning Analytics

2022

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As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they may be reluctant to draw causal inferences. The past decades have seen much progress concerning causal inference in the absence of experimental data. This paper introduces directed acyclic graphs (DAGs), an increasingly popular tool to visually determine the validity of causal claims. Based on this, three basic pitfalls are outlined: confounding bias, overcontrol bias, and collider bias. Further, the paper shows how these pitfalls may be present in the published LA literature alongside possible remedies. Finally, this approach is discussed in light of practical constraints and the need for theoretical development.

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“…It aims to regroup and promote high-quality research in the field, creating a forum for challenges and novel advancements in AI in education to be explored. While a great deal of research has been presented in the field (Krouska et al, 2020 ; Anwar, 2021 ; Mousavi et al, 2021 ; Schaldenbrand et al, 2021 ; Sense et al, 2021 ; Pelánek, 2022 ; Rebolledo-Mendez et al, 2022 ; Zhou et al, 2022 ), there is a significant room for improvement in this direction. This Research Topic focuses on triggering an exchange of ideas in the field and reinforcing and expanding the network of researchers, academics, and market representatives.…”

mentioning

confidence: 99%

Editorial: Artificial intelligence techniques for personalized educational software

Troussas¹,

Krouska²,

Kabassi³

et al. 2022

Front. Artif. Intell.

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Assessing the Effectiveness of Student Advice Recommender Agent (SARA): the Case of Automated Personalized Feedback

Cited by 19 publications

References 33 publications

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective

Causal Inference and Bias in Learning Analytics

Editorial: Artificial intelligence techniques for personalized educational software

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