Classification of Students’ Misconceptions in Individualised Learning Environments (C-SMILE): An Innovative Assessment Tool for Engineering Education Settings

Subheesh, N. P.; Sobin, C. C.; Ali, Jahfar; Varsha, M.

doi:10.1109/educon52537.2022.9766572

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…This study has shown the potential of designing inquiry leaning environments according to known misconceptions of students. A next step could be to determine which misconceptions each individual student possesses and present the student with assignments addressing only those individual misconceptions (see e.g., Subheesh et al, 2022). Longitudinal research to trace the conceptual change process could also be done.…”

Section: Discussion and Future Workmentioning

confidence: 99%

The impact of an online inquiry-based learning environment addressing misconceptions on students' performance

Siantuba

Nkhata

Jong

2023

Smart Learn. Environ.

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This study sought to develop and evaluate an online module based on inquiry learning with digital laboratories, which was intended to address students’ misconceptions in a science domain. In a quasi-experimental design, 171 first-year students in a higher education introductory physics course on circular motion were as their existing groups assigned to an experimental (n = 100) or a control (n = 71) condition. The experimental condition was developed by arranging online inquiry activities that would encourage students to probe five identified misconceptions. The control condition required students to engage in online inquiry following the traditional syllabus outline. Students in both conditions used the same type of digital laboratory setup. The participants learned about the topic of circular motion and their knowledge was assessed. Results of the knowledge test revealed that the experimental condition geared towards addressing students’ misconceptions facilitated conceptual change more than the control condition.

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Section: Discussion and Future Workmentioning

confidence: 99%

The impact of an online inquiry-based learning environment addressing misconceptions on students' performance

Siantuba

Nkhata

Jong

2023

Smart Learn. Environ.

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“…Based on the development of students' intelligence, innovation ability is a comprehensive ability to find and solve new problems and innovate things [1][2][3][4]. Our society today need talents with innovation ability, because this ability is a necessary 232 http://www.i-jet.org characteristic of talents in the information age [5][6][7][8][9]. Cultivating talents with innovation ability is both the goal of higher vocational colleges and their responsibility given by the era [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Practical Knowledge Level Characteristics and Innovative Practice Achievement Transformation Mechanism of Vocational College Students

Gao¹

2023

Int. J. Emerg. Technol. Learn.

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Cultivating talents with innovation ability is both the goal of higher vocational colleges and their responsibility given by the era. Focusing on improving the practical knowledge level of students means that students should be exposed to nature, society and problems through practical activities, thus providing the maximum space for them to think, explore, discover and innovate. As for existing studies on cultivating innovative ability of students and improving their practical knowledge level, they mainly have focused on ability evaluation and analysis of influencing factors, and few of them has involved analysis of their practical knowledge level characteristics. Therefore, this paper studied the characteristics and transformation mechanism of their innovative practice achievements. A multi-level comprehensive evaluation system was constructed, which aimed to evaluate the practical knowledge level of those students. The input dimension of quantized data of indexes was reduced in accordance with the principal component analysis (PCA). Then the quantized data of the low-dimensional indexes were mined by hierarchical clustering, and different training subsets were generated to participate in the evaluation model training. Based on the idea of clustering-prediction-integration, Gated Recurrent Unit (GRU) model was used for nonlinear integration of the output results of two-way Long Short-Term Memory (LSTM) model, which further fit the nonlinear characteristics in the quantized data of all indexes. By introducing Bootstrap-Data Envelopment Analysis (DEA) method, the transformation rate of innovative practice achievements of the students was collected and calculated in order to obtain a small calculation error. Experimental results verified the effectiveness of the proposed calculation method of the model.

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Artificial intelligence applications in education: Natural language processing in detecting misconceptions

Kökver,

Pektaş,

Çelik

2024

Educ Inf Technol

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This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design cycle was used. The dataset obtained from 402 teacher candidates was analysed by Natural Language Processing (NLP) methods. Data was classified using Machine Learning (ML), one of the AI tools, and supervised learning algorithms. It was concluded that 175 teacher candidates did not have sufficient knowledge about the concept of greenhouse effect. It was found that the AI algorithm with the highest accuracy rate and used to predict teacher candidates’ misconceptions was Multilayer Perceptron (MLP). Furthermore, through the Enhanced Ensemble Model Architecture developed by researchers, the combination of ML algorithms has achieved the highest accuracy rate. The kappa (κ) value was examined in determining the significant difference between the AI algorithm and the human expert evaluation, and it was found that there was a significant difference, and the strength of agreement was significant according to the research findings. The findings of the current study represent a significant alternative to the prevailing pedagogical approach, which has increasingly come to rely on information technologies in the process of improving conceptual understanding through the detection of conceptual misconceptions. In addition, recommendations were made for future studies.

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Classification of Students’ Misconceptions in Individualised Learning Environments (C-SMILE): An Innovative Assessment Tool for Engineering Education Settings

Cited by 5 publications

References 14 publications

The impact of an online inquiry-based learning environment addressing misconceptions on students' performance

The impact of an online inquiry-based learning environment addressing misconceptions on students' performance

Practical Knowledge Level Characteristics and Innovative Practice Achievement Transformation Mechanism of Vocational College Students

Artificial intelligence applications in education: Natural language processing in detecting misconceptions

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