A Collaborative Filtering Based Approach for Recommending Elective Courses

Ray, Sanjog; Sharma, Anuj

doi:10.1007/978-3-642-19423-8_34

Cited by 41 publications

(28 citation statements)

References 13 publications

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“…Loll and Pinkwart [15] employ collaborative filtering to score student assignment solutions based on peer evaluation, and show that it is as effective as manual assessment. Finally, similar to our work, Ray and Sharma [16] propose using collaborative filtering to predict student course grades. Our work differs from [16] in the following aspects:…”

Section: Related Workmentioning

confidence: 70%

“…Finally, similar to our work, Ray and Sharma [16] propose using collaborative filtering to predict student course grades. Our work differs from [16] in the following aspects:…”

Section: Related Workmentioning

confidence: 70%

“…In [16], the authors only focus on a relatively narrower problem of estimating student success in elective courses.…”

Section: Related Workmentioning

confidence: 99%

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Predicting Student Success in Courses via Collaborative Filtering

Çakmak

2017

ijisae

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Based on their skills and interests, students' success in courses may differ greatly. Predicting student success in courses before they take them may be important. For instance, students may choose elective courses that they are likely to pass with good grades. Besides, instructors may have an idea about the expected success of students in a class, and may restructure the course organization accordingly. In this paper, we propose a collaborative filtering-based method to estimate the future course grades of students. Besides, we further enhance the standard collaborative filtering by incorporating automated outlier elimination and GPA-based similarity filtering. We evaluate the proposed technique on a real dataset of course grades. The results indicate that we can estimate the student course grades with an average error rate of 0.26, and the proposed enhancements improve the error value by 16%.

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Section: Related Workmentioning

confidence: 70%

“…Finally, similar to our work, Ray and Sharma [16] propose using collaborative filtering to predict student course grades. Our work differs from [16] in the following aspects:…”

Section: Related Workmentioning

confidence: 70%

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Predicting Student Success in Courses via Collaborative Filtering

Çakmak

2017

ijisae

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“…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: 99%

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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“…Within the context of developing methods to predict the next-term grades, most existing approaches [4,7,8,19] rely on neighborhood-based collaborative filtering methods. For each student whose grade needs to be predicted, a set of similar students are identified that have already taken that course and their grades are used to estimate the desired grade via some similarity-weighted aggregation function.…”

Section: Related Workmentioning

confidence: 99%

Grade prediction with models specific to students and courses

Polyzou

Karypis

2016

Int J Data Sci Anal

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The accurate estimation of students' grades in future courses is important as it can inform the selection of next term's courses and create personalized degree pathways to facilitate successful and timely graduation. This paper presents future course grade predictions methods based on sparse linear and low-rank matrix factorization models that are specific to each course or student-course tuple. These methods identify the predictive subsets of prior courses on a course-by-course basis and better address problems associated with the not-missing-at-random nature of the student-course historical grade data. The methods were evaluated on a dataset obtained from the University of Minnesota, for two different departments with different characteristics. This evaluation showed that focusing on course-specific data improves the accuracy of grade prediction.

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A Collaborative Filtering Based Approach for Recommending Elective Courses

Cited by 41 publications

References 13 publications

Predicting Student Success in Courses via Collaborative Filtering

Predicting Student Success in Courses via Collaborative Filtering

An Architecture for Recommendation of Courses in E-learning

Grade prediction with models specific to students and courses

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