Recommending Physics Exercises in Moodle Based on Hierarchical Competence Profiles

Tödtli, Beat; Laner, Monika; Semenov, Jouri; Paoli, Beatrice

doi:10.1007/978-3-319-45153-4_88

Cited by 2 publications

(4 citation statements)

References 4 publications

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“…The performance of students of cluster avg is in between not active and active cluster. This cluster is characterized by average marks obtained in quiz (14), average number of assignment done(5), average number of messages sent to teacher (33), average number of messages sent using forum (29), average marks obtained in quiz (4 We have proposed an architecture for the recommendation of courses based on their profile. The profile is created using k-means algorithm.…”

Section: Transform the Datamentioning

confidence: 43%

“…In yet another work the authors in [14] introduced a prototype for an adaptive navigation system which uses a dissimilarity measure between student and exercise profiles to rank and recommend exercises. In [15] they narrated technical activity based course recommender system in which they defined an architectural model of this method using a collaborative filtering technique which is based on collecting and analyzing information about user activity.…”

Section: Related Workmentioning

confidence: 45%

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An Architecture for Recommendation of Courses in E-learning

Dwivedi¹,

Rawat²

2017

IJITCS

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Abstract-Over the last few years, the face of traditional learning has changed significantly, due to the emergence of the web. Consequently several learning systems have emerged such as computer-based learning, web-based learning among others, meeting different kinds of educational needs of the learners and educators as well. E-learning systems allow educators, distribute information, create content material, prepare assignments, engage in discussions, and manage distance classes among others. They accumulate a huge amount of data as a result of learner's interaction with the site. This data can be used to find students' learning pattern based on which appropriate courses could be recommended to them. However existing approaches of recommending courses to learner offer the same course to all the learners irrespective of their knowledge and skill level which results in decreasing their academic performance. This paper proposes an architecture for the recommendation of courses to a learner based on his/her profile. The profile of a learner is created by applying k-means algorithm to learner's interaction data in moodle. The results show that the non active learners should not be recommended advanced courses if they have obtained poor marks and are not active in the concern course. In the initial stage we discover learners' performance in data mining course which will further be extended to other courses as well.

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Section: Transform the Datamentioning

confidence: 43%

Section: Related Workmentioning

confidence: 45%

An Architecture for Recommendation of Courses in E-learning

Dwivedi¹,

Rawat²

2017

IJITCS

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“…In recent years, with the increasing demand for personalized teaching in primary and secondary education, various exercise recommendation algorithms emerge. At present, the existing exercise recommendation algorithms mainly include exercise recommendation algorithm based on personalized difficulty [1][2][3][4][5][6] , exercise recommendation algorithm oriented to students' weak links [7][8] , and exercise recommendation algorithm aiming at improving students' learning effect [9][10] .…”

Section: Related Workmentioning

confidence: 99%

“…(6) 100 students were randomly selected, and 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-70, 70-80, 80-90, and 90-100 were used as the predicted scoring interval, and exercises were recommended for these 100 students. For each predicted scoring interval, perform one round of steps ( 7), (8), and ( 9). ( 7) For each student who needs to recommend exercises, feedforward neural network is used to calculate the expected score of each problem for the student, and the exercises with the expected score within the specified range are recommended to the student.…”

Section: Experiments Processmentioning

confidence: 99%

A personalized exercise recommendation method for teaching objectives

Li¹,

Fei²

2022

International Conference on Computer Application and Information Security (ICCAIS 2021)

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It can improve the learning effect and reduce the learning burden to recommend exercises which are in accordance with the teaching objectives to students. How to accurately judge the needs of students and recommend exercises that are consistent with the teaching objectives is an urgent problem to be solved in the field of personalized education. This paper proposes a personalized exercise recommendation method for teaching objectives. This method can recommend exercises that are highly compatible with the syllabus for students according to their selected knowledge points and expected score range. According to the experimental test, the average prediction success rate of this method is 72.5%, 57.2% higher than the exercise recommendation method based on collaborative filtering and KNN, and 10.6% higher than the exercise recommendation method based on cognitive diagnosis and probability matrix decomposition.

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Recommending Physics Exercises in Moodle Based on Hierarchical Competence Profiles

Cited by 2 publications

References 4 publications

An Architecture for Recommendation of Courses in E-learning

An Architecture for Recommendation of Courses in E-learning

A personalized exercise recommendation method for teaching objectives

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