Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments

Zhang, Ningyu; Biswas, Gautam; Hutchins, Nicole

doi:10.1007/s40593-021-00275-x

Cited by 7 publications

(3 citation statements)

References 101 publications

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“…Tabla 1: Trabajos relacionados. Artículos 1 2 3 4 5 (Tarus et al, 2018) X X (Anwar y Uma, 2022) X (Zheng et al, 2019) X X (Wong et al, 2019) X X (Liu et al, 2018) X X X (Qu et al, 2019) X X (Klašnja-Milićević et al, 2018) X X (Zhu et al, 2019) (Deeva y De Weerdt, 2019) (Taub et al, 2018) X X (Wan y Niu, 2020) X X (Al-Twijri et al, 2022) (Taub y Azevedo, 2018b) X X (Norm Lien et al, 2020) X X (Yildirim y Usluel, 2022) X X X (Zhang et al, 2022) X X X (Shih, 2018) X X (Song et al, 2022) X X (Wang y Zaïane, 2018) (Mudrick et al, 2018) X X (Taub y Azevedo, 2018a) X X (Yang, 2021) (Bermudez et al, 2020) (Cheng Tan et al, 2020) X X (Malekian et al, 2020) X X (Kong y Pollock, 2020) X X (Latypova, 2022) X X X (Fatahi et al, 2018) X X X (Pogorskiy y Beckmann, 2022) X (He et al, 2021) X (Chen y Wang, 2020) X (Czibula et al, 2019) X X (Doko et al, 2018) X X (Real et al, 2021) X (Song y Ye, 2021) (Cheng et al, 2021) X (Niemeijer et al, 2020) X (Aktas y Aktas, 2021)…”

Section: Metodología De Análisismentioning

confidence: 99%

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

Reyes-García,

Rodríguez-Mazahua,

Pacheco-Ortiz

et al. 2023

ICBI

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En este trabajo se muestran los resultados de un análisis de 38 artículos de investigación de las principales editoriales informáticas, para comparar los métodos de minería de patrones secuenciales que utilizaron, además se propone un sistema de evaluación adaptativa computarizada (CAT) basada en reglas de asociación secuenciales. Anteriormente, se presentó un sistema CAT que integra reglas de asociación como un método de selección de ítems. En este caso, el sistema suministrará a los estudiantes ítems que correspondan a su nivel de conocimiento a través de las repuestas que ellos ingresen en la prueba, además de las respuestas correctas de otros estudiantes en la misma prueba basadas en reglas de asociación secuenciales con mayor soporte. Las tecnologías propuestas para el desarrollo del sistema son Java, y el entorno de desarrollo NetBeans, además del marco de trabajo JavaServer Faces, el sistema gestor de bases de datos MySQL, apegado a la metodología de ingeniería web basada en UML (UWE por sus siglas en inglés).

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Section: Metodología De Análisismentioning

confidence: 99%

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

Reyes-García,

Rodríguez-Mazahua,

Pacheco-Ortiz

et al. 2023

ICBI

View full text Add to dashboard Cite

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“…Cognitive strategies are typically goal-directed and situation-specific, for example, read to find a specific piece of information (Weinstein & Meyer, 1994). Metacognitive strategies involve more generally applicable processes that include planning, monitoring, and reflecting (Donker et al, 2014;Zhang et al, 2021). While cognitive strategies focus on our skills and operate on the knowledge of 'objects' (Winne, 1995), metacognitive learning strategies involve deliberation on the use of particular cognitive processes and combining these processes to accomplish larger tasks (Winne & Hadwin, 2008).…”

Section: The Conceptual Frameworkmentioning

confidence: 99%

Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours

Munshi

Biswas

Baker

et al. 2022

Computer Assisted Learning

Self Cite

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Background Providing adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks. Objectives This paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs. Methods We detect key cognitive/metacognitive inflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain. Results and Conclusions Adaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students. Implications This paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.

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“…The scope of STS has been envisioned to be extended to include not only the designing of instructional materials but also the provision of recommendations concerning content and skill development plans for students. This work presents an STS based on CBR data analysis of learning techniques coupled with blended knowledge of engineering techniques that supports learners' competency analysis through adaptive planning and realistic content recommendation in various domains such as language education, programming, maths, and science [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Smart education system to improve the learning system with CBR based recommendation system using IoT

M.R.M,

Dabade,

et al. 2023

Heliyon

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Measuring and Analyzing Students’ Strategic Learning Behaviors in Open-Ended Learning Environments

Cited by 7 publications

References 101 publications

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

Hacia el desarrollo de un sistema de evaluación adaptativa computarizada basada en reglas de asociación secuenciales

Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours

Smart education system to improve the learning system with CBR based recommendation system using IoT

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