Phân tích học tập để dự đoán hiệu suất học tập của sinh viên: Nghiên cứu trường hợp từ nền tảng học tập cộng tác dựa trên neurodidactics

Springer Science and Business Media LLC - Tập 27 - Trang 12913-12938 - 2022
Carlos Javier Pérez Sánchez1, Fernando Calle-Alonso2, Miguel A. Vega-Rodríguez3
1Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain
2Departamento de Didáctica de las Ciencias, Universidad de Extremadura, Cáceres, Spain
3Departamento de Tecnología de los Computadores y de las Comunicaciones, Universidad de Extremadura, Cáceres, Spain

Tóm tắt

Trong công trình này, 29 đặc trưng đã được định nghĩa và triển khai để được tự động trích xuất và phân tích trong bối cảnh NeuroK, một nền tảng học tập nằm trong khuôn khổ của neurodidactics. Neurodidactics là một khuôn khổ giáo dục giải quyết tối ưu hóa quá trình học tập và giảng dạy từ góc độ chức năng của não bộ. Trong bối cảnh này, các đặc trưng được trích xuất có thể được đưa làm đầu vào cho nhiều thuật toán học máy khác nhau để dự đoán hiệu suất của sinh viên. Phương pháp đề xuất đã được thử nghiệm với dữ liệu từ một khóa học quốc tế với 698 sinh viên. Độ chính xác lớn hơn 0.99 đã được đạt được trong việc dự đoán hiệu suất cuối cùng của sinh viên. Mô hình tốt nhất được thực hiện với thuật toán Random Forest. Nó đã lựa chọn 7 đặc trưng có liên quan, tất cả đều có thể được diễn giải rõ ràng trong quá trình học tập. Những đặc trưng này liên quan đến các nguyên tắc của neurodidactics, và phản ánh tầm quan trọng của một cách tiếp cận học tập xã hội và xây dựng trong bối cảnh này. Công trình này là một bước đầu tiên trong việc liên kết các công cụ phân tích học tập với neurodidactics. Phương pháp này, sau khi được điều chỉnh để nắm bắt các đặc trưng liên quan tương ứng với các bối cảnh khác nhau, có thể được triển khai trên các nền tảng quản lý học tập khác, và áp dụng cho các khóa học trực tuyến khác với mục tiêu dự đoán hiệu suất của sinh viên, bao gồm theo dõi quá trình tiến bộ và rủi ro bỏ học của họ theo thời gian thực.

Từ khóa

#neurodidactics #phân tích học tập #thuật toán học máy #dự đoán hiệu suất sinh viên #học tập xã hội #học tập xây dựng

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