Deep learning on curriculum study pattern by selective cross join in advising students' study path

Matulatan, Tekad; Resha, Muhammad

doi:10.1109/eecsi.2017.8239159

Cited by 3 publications

(1 citation statement)

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“…Chakraborty et al [18] reported a content-based mining approach which goes through all relevant institutional data storage facilities and extracts required information in order determine the most accurate recommendation list of scholars based on a comparative analysis (Latent Dirichlet Allocation, LDA, Hierarchical Dirichlet Process, HDP, Latent Semantic Analysis, LSA, and Clustering techniques: k-means and Hierarchical Clustering). In reference [19], Matulatan and Resha offered a Monte Carlo tree type style search to help a students' advisor in analyzing students' performance based on the actual academic progress records of the students. It is augmented with the inclusion of a facility to build course patterns based on the performance records of previous subject-specific students.…”

Section: Related Workmentioning

confidence: 99%

Considering Students’ Abilities in the Academic Advising Process

2020

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Academic advising is time-consuming work. At the same time, it needs to be efficient and productive in assisting the students to choose appropriate academic courses towards the completion of their selected programs in a beneficial manner. In addition, both private and public educational institutions are, currently, operating in an extremely competitive market and are, thus, faced with various challenges. Among these are the twin challenges of student retention and the rate of success in completion of their chosen academic courses. The mentioned challenges have a direct bearing on the quality of academic advising and services provided to the students, by the individual academic institution. A number of research studies have been carried out suggesting various online academic advising systems for undergraduate and graduate programs. In this context, we develop and present, here, an academic advising system which differs from and improves upon previously suggested methodologies with the inclusion of the facility to track individual students’ performance and, thus, ability in educational subjects and programs, taken in the previous academic terms. Our suggested methodology is based on the use of this facility to guide students in the selection of courses that they may register for the forthcoming academic term. We believe that the consideration of individual students’ past academic preformation, in our suggested methodology, is a significant improvement and will assist students in making more beneficial choices when registering for academic courses.

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Section: Related Workmentioning

confidence: 99%