Feedback is crucial during programming problem solving, but context often lacks critical and difference. Generative artificial intelligence dialogic feedback (GenAIDF) has the potential to enhance learners' experi...
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Feedback is crucial during programming problem solving, but context often lacks critical and difference. Generative artificial intelligence dialogic feedback (GenAIDF) has the potential to enhance learners' experience through dialogue, but its effectiveness remains sufficiently underexplored in empirical research. This study employed a rigorous quasi-experimental design and collected multidimensional data through mixed methods to investigate the impact of GenAIDF at different stages of programmingproblem-solving on high school students' programming skills and critical thinking. One hundred seventy-two high school students from four distinct classes participated in this study. We established three experimental groups, introducing GenAIDF during the code writing (CAG, NCAG = 43), verification debugging (DAG, NDAG = 43), and both code writing and verification debugging (CDAG, NCDAG = 43) stages, and one control group, without GenAIDF introduced at any stage (NAG, NNAG = 43). The results indicated that, first, in terms of programming skills, the three experimental groups exhibited no significant difference in their programming knowledge, yet they significantly outperformed the control group. CAG excelled in programming project performance, while DAG excelled in structure. CDAG excelled in functions but had poor plagiarism scores. Second, regarding critical thinking skills, DAG performed best, followed by CAG, CDAG, and NAG, with significant differences observed among the four groups. Finally, student interviews revealed increased learning engagement, satisfaction, and critical thinking consciousness. Based on these findings, the study provides empirical recommendations for teachers on effectively utilizing GenAIDF in the future.
A new and emergent field in an educational context is the use of Brain-Computer Interaction (BCD technology to better understand and promote learning processes. In this context, the idea is to obtain information about...
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ISBN:
(纸本)9781538695067
A new and emergent field in an educational context is the use of Brain-Computer Interaction (BCD technology to better understand and promote learning processes. In this context, the idea is to obtain information about the user and deduce his/her mental states (e.g. workload, attention, concentration) through user's electroencephalogram signals (EEG). In future researches, we are interested in understanding the performance of users in tasks involving high cognitive processes such as programming problem solving. The goal would be to find metrics and strategies that arc adaptable to each user, looking to increase the success in programming learning. However, this work represents a set of initial works that provides an overview of how brain computer interaction can intersect with issues in the field of education, namely in design and in cognitive attention and concentration processes. The main goal of this study is to analyse several cognitive parameters (Attention, Concentration) of crucial importance for learning, while students do a programming problem solving oriented task activity. For a better characterization of the data, an analysis of the ERD/ERS complex was performed, thus analysing the event synchronization or desynchronization, in order to reflect the activation or inhibition of the cerebral activity during the game and consequently the absorption of information and the capacity of learning. Additionally, five EEG features were extracted, namely the powers of Delta, Theta, Alpha, Beta and Gamma bands, as well as, the variability of these bands' energy.
This work offers an outline of how brain computer interactions can interconnect with education, specifically with regard to the cognitive and emotional processes occurring during difficult learning. We believe that un...
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This work offers an outline of how brain computer interactions can interconnect with education, specifically with regard to the cognitive and emotional processes occurring during difficult learning. We believe that understanding how to optimize the learner's attention and workload in learning tasks can improve the efficacy of educational processes, especially in tasks involving highly cognitive activities, such as programming problem solving. The main objective of this study was to examine several brain parameters (attention, concentration and the energy of several brain waves) in a programming orientated task, as well as their variability during tasks of varying complexity. We consider that this work presents very promising future developments, including the possibility of incorporating this technology into a customised automatic system adapted to the student's cognitive and emotional state.
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