Artificial Intelligence in Education

Conati, Cristina; Heffernan, Neil T.; Mitrović, Antonija; Verdejo, Felisa

doi:10.1007/978-3-319-19773-9

Cited by 27 publications

(17 citation statements)

References 28 publications

(41 reference statements)

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“…This large difference is most likely due to the fact that our recent study involved students at a regular, urban middle school whereas the other study involved students at a magnet school for high-achievers. Although it is difficult to quantitatively compare the transition results from [12] to these new results because the cluster centroids are different, the results are fairly qualitatively consistent. In both studies, there were more transitions to the same cluster (i.e., non-transitions) than would be predicted by a random model.…”

Section: Comparison To Previous Worksupporting

confidence: 58%

“…We modeled the students' behavior with coherence analysis-based metrics [7] and clustered the resulting feature vectors to reveal commonalities in their problem solving approaches. We compared the resulting clusters to those in a different study [12] and we identified four of the five student learning behavior clusters found by that research. The missing cluster, engaged and efficient students, was likely missing due to the different student populations at the magnet school.…”

Section: Discussionmentioning

confidence: 93%

“…Thus, these learners will perform a larger proportion of supported actions and take advantage of a larger proportion of the potential that their actions generate. In this paper, we incorporated the following six CA metrics (identical to those used in [12]): Equivalent metrics could be applied to any OELE where a student (1) can take actions to gain information and (2) makes discrete edits to an artifact which can be linked to gained information.…”

Section: Methodsmentioning

confidence: 99%

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Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments

Gauch

Biswas

2016

Intelligent Tutoring Systems

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Abstract. Open-ended computer-based learning environments (OELEs) can be powerful learning tools in that they help students develop effective self-regulated learning (SRL) and problem solving skills. In this study, middle school students used the SimSelf OELE to build causal models to learn about climate science. We study their learning and model building approaches by calculating a suite of behavioral metrics derived using coherence analysis (CA) that are used as features on which to group students by their type of learning behavior. We also analyze changes in these metrics over time, and compare these results to results from other studies with a different OELE to see determine generalizable their findings are across different OELE systems.

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Section: Comparison To Previous Worksupporting

confidence: 58%

Section: Discussionmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

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Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments

Gauch

Biswas

2016

Intelligent Tutoring Systems

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“…This new learning environment is enriched with digital, adaptive and context-aware resources that can provide personalized and inclusive learning experiences to enhance teaching and learning in an online learning paradigm [3][4][5]. The students can use a smart learning environment and digital learning devices to support their learning strategies in order to enhance their learning experiences in an online learning environment, and one such strategy is self-regulated learning (SRL), which is defined as selfgenerated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal learning goals in a subject [6]. It is well established that students' ability to self-regulate their learning process can enhance skill and performance [7,8].…”

Section: A Backgroundmentioning

confidence: 99%

New Development and Evaluation Model for Self-Regulated Smart Learning Environment in Higher Education

Gambo

Shakir

2019

2019 IEEE Global Engineering Education Conference (EDUCON)

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The smart learning environment is a technologysupported learning environment that is enriched with digital resources, context-aware and adaptive devices, and can provide appropriate support to meet the learning style and abilities of diverse students to promote better and enhances learning process in higher education. It is a student-centered learning environment that has the capacity to engage students and offers an effective learning process. It is characterized by the ability to provide interactions between students and facilitators, and personalized and inclusive learning experiences to anyone, anytime and anywhere using smart mobile devices. However, despite the increasing use of smart learning environment in higher education, there is no well-defined model with a set of educational requirements for developing and evaluating selfregulated smart learning environment which considers both instructional and evaluation design. Therefore, this article explores instructional design and learning evaluation process to propose a new set of educational requirements referred to as smart learning environment pedagogical and educational requirements model (SLE-PERM) for developing and evaluating self-regulated smart learning environment. Next, the article presents the application of the educational requirements on three common learning management system (LMS) namely: Moodle, Blackboard and Schoology which can be used to evaluate the suitability of LMS to guide stakeholders on the critical issues that require pedagogical and educational requirements for developing a self-regulated smart learning environment in higher education.

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“…The smart learning environment is a responsive, proactive, and context-aware learning environment. Therefore, it is of interest to explore how SRL can support a learner to develop thinking strategies and promotes metacognitive and motivations towards achieving learning goals (Pérez-Álvarez et al, 2018;Durán-Sánchez et al, 2018;John et al, 2015;Zimmerman, 2002). The student's ability to control their learning process can enhance the educational process and provide the support needed to succeed in an online learning environment (Pérez-Álvarez et al, 2018;Spector, 2016;Zimmerman, 2002).…”

Section: Introductionmentioning

confidence: 99%

Review on self-regulated learning in smart learning environment

Gambo

Shakir

2021

Smart Learn. Environ.

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Despite the increasing use of the self-regulated learning process in the smart learning environment, understanding the concepts from a theoretical perspective and empirical evidence are limited. This study used a systematic review to explore models, design tools, support approaches, and empirical research on the self-regulated learning process in the smart learning environment. This review revealed that there is an increasing body of literature from 2012 to 2020. The analysis shows that self-regulated learning is a critical factor influencing a smart learning environment’s learning process. The self-regulated learning components, including motivation, cognitive, metacognitive, self-efficiency, and metacognitive components, are most cited in the literature. Furthermore, self-regulated strategies such as goal setting, helping-seeking, time management, and self-evaluation have been founded to be frequently supported in the literature. Besides, limited theoretical models are designed to support the self-regulated learning process in a smart learning environment. Furthermore, most evaluations of the self-regulated learning process in smart learning environment are quantitative methods with limited mixed methods. The design tools such as visualization, learning agent, social comparison, and recommendation are frequently used to motivate students’ learning engagement and performance. Finally, the paper presents our conclusion and future directions supporting the self-regulated learning process in the smart learning environment.

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Artificial Intelligence in Education

Cited by 27 publications

References 28 publications

Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments

Behavior Changes Across Time and Between Populations in Open-Ended Learning Environments

New Development and Evaluation Model for Self-Regulated Smart Learning Environment in Higher Education

Review on self-regulated learning in smart learning environment

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