Applying HOL/PBL to Prepare Undergraduate Students into Graduate Level Studies in the Field of Aerospace Engineering Using the Puerto Rico CubeSat Project Initiative

Darbali-Zamora, Rachid; Cobo-Yepes, Nicolas; Ortiz-Rivera, Eduardo I.; Aponte-Bezares, Erick E.; Rincon-Charris, Amilcar A.

doi:10.1109/fie.2018.8659049

Cited by 2 publications

(1 citation statement)

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“…College students' learning enthusiasm is the attitude of active participation, active exploration and continuous engagement shown by college students in the learning process, and it is the internal motivation that directly promotes their learning [1][2][3][4][5]. Contemporary college students have a certain degree of autonomy in their learning activities, that's, they have the right and freedom to choose independently in terms of learning content, learning methods and learning time [6][7][8][9][10][11]. Although college students also need the guidance and management of teachers in learning, their knowledge structure has changed from the vertical accumulation in the middle school stage to the horizontal expansion in the university stage [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Students' Learning Motivation Based on Combinatorial Optimization Algorithm

Deng¹,

Lin²

2023

Int. J. Emerg. Technol. Learn.

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The strength of college students' learning enthusiasm directly affects their learning effect. Studying and predicting students' learning enthusiasm has positive significance for improving college students themselves and college education. Existing methods have not involved how to select and extract features related to students' learning enthusiasm, so it is difficult to meet the needs of learning enthusiasm prediction. Therefore, this article takes English learning as an example to conduct a research on the prediction model of students' learning enthusiasm based on combinatorial optimization algorithm. Taking English learning as an example, this article expounds in detail the main manifestations of students' learning enthusiasm, and predicts the intention of students to participate in learning behaviors based on the enumerated behaviors, so as to judge students' learning enthusiasm, and gives the construction method of the prediction model. This study constructs a network model for the evaluation of students' learning autonomy, and finally outputs the grade results of students' learning autonomy evaluation. Based on the obtained predictions of students' participation in learning behavior intentions and the evaluation results of students' learning autonomy, a combined model is established through the weighting of the inverted error method to predict the regression of students' learning enthusiasm. Experimental results verify the effectiveness of the constructed single model and combined model.

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