Knowledge-Based Major Choosing Decision Making for Remote Students

Zhou, Qing; Yu, Fang

doi:10.1109/csse.2008.379

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…Student-oriented advising tools are less common. One relevant example in this category is KMCD [70], a self-advising system that shows courses for enrollment based on a given curriculum design. CARTA [70] is another course planning tool that provides students with course descriptive information, evaluations of instructors, and grade distributions.…”

Section: Visual Learning Analytics and Tools For Academic Advisingmentioning

confidence: 99%

Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences

Méndez

Galárraga

Chiluiza

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools. CCS CONCEPTS• Human-centered computing → Empirical studies in visualization.

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Section: Visual Learning Analytics and Tools For Academic Advisingmentioning

confidence: 99%

Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences

Méndez

Galárraga

Chiluiza

2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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“…In this section, we review the model of KMCD proposed in [24]. The student user is assumed to be Jack for simplification.…”

Section: Model Of the Systemmentioning

confidence: 99%

“…Among them the most popular is curriculum sequencing [5], [8], [16]- [23], which provides the student with either knowledge sequencing (the most suitable individually planned sequence of knowledge units to learn) or task sequencing (sequence of learning tasks to work with). These approaches, as pointed out in [24], are of great use for remote students in helping them find an "optimal path" through the learning material, but before that they must know full well what goal (e.g., choosing a major) is expected to attain. Knowledge-based major choosing decision (KMCD) is such a system that provides remote students a personalized service for their major choosing [24].…”

mentioning

confidence: 99%

“…These approaches, as pointed out in [24], are of great use for remote students in helping them find an "optimal path" through the learning material, but before that they must know full well what goal (e.g., choosing a major) is expected to attain. Knowledge-based major choosing decision (KMCD) is such a system that provides remote students a personalized service for their major choosing [24]. It helps students find those majors which are supposed to fit in with their learning abilities and to have bright career futures.…”

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confidence: 99%

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A First-Order Logic Framework of Major Choosing Decision Making With an Uncertain Reasoning Function

Zhou

2018

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes an auto-decision making system, knowledge-based major choosing decision (KMCD), which helps remote students choose their majors. It is built within a first-order reasoning system with an uncertain reasoning function. The knowledge of both students who want to choose their majors and who have finished their studies are used in the decision making process of the system. KMCD provides personalized suggestions to those students who want to choose majors. Both the learning abilities of students and the career prospects of majors are carefully analyzed on the basis of the knowledge in database. As these analyzing results are well utilized in the decision making process of KMCD, it can be expected that the students who take the suggestions will have a better chance to get graduated smoothly and start their careers successfully after graduation. A detailed description of KMCD is given, which shows that the system is actually a reasonable decision making framework with a capability of expansion of other knowledge which is supposed to be useful for major choosing. An experimental study on data of real college students is devised to justify the effectiveness of KMCD.Index Terms-Decision making, first-order logic, knowledgebased, major choosing, supporting degree.

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“…Authors in [7,36], implemented a case based reasoning (CBR) system that recommends to the candidate the most suitable major, after comparing the historical cases by the student case. Paper [42] presents two novel nearest neighbor-like classification algorithms for program recommendation, which provide a program planning service to academic advisors and students of post-secondary institutions, while paper [32] proposes an auto-decision system, which helps distance education students choose their majors. Furthermore, the basic idea in paper [29] is to design a model for testing and measuring the student capabilities like intelligence, understanding, comprehension, and mathematical concepts, plus his/her past academic record and his/her intelligence level, and applying the results to a rule-based decision support system to determine the compatibility of those capabilities with the available faculties/majors.…”

Section: Choosing Programs/majorsmentioning

confidence: 99%

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

2017

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This paper aims to provide the reader with a comprehensive background for understanding current knowledge on Academic Advising Systems (AAS) and its impact on learning. It constitutes an overview of empirical evidence behind key objectives of the potential adoption of AAS in generic educational strategic planning. The researchers examined the literature on experimental case studies conducted in the domain during the past ten years (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017). Search terms identified 98 mature pieces of research work, but inclusion criteria limited the key studies to 43. The authors analyzed the research questions, methodology, and findings of these published papers and categorized them accordingly. The results have highlighted three distinct major directions of the AAS empirical research. This paper discusses the emerged added value of AAS research and highlights the significance of further implications. Finally, the authors set their thoughts on possible uncharted key questions to investigate both from pedagogical and technical considerations.

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Knowledge-Based Major Choosing Decision Making for Remote Students

Cited by 7 publications

References 6 publications

Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences

Showing Academic Performance Predictions during Term Planning: Effects on Students’ Decisions, Behaviors, and Preferences

A First-Order Logic Framework of Major Choosing Decision Making With an Uncertain Reasoning Function

Academic Advising Systems: A Systematic Literature Review of Empirical Evidence

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