A Multi-stage Approach to Personalized Course Selection and Scheduling

Morrow, Tyler; Hurson, Ali R.; Sarvestani, Sahra Sedigh

doi:10.1109/iri.2017.58

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Morrow et al [16,17] proposed a personalized course selection and scheduling model which is an extension to their previous model, PERCEPOLIS. The courses are prioritized based on an integer linear model, and a scheduling problem is iteratively solved for semesters using a linear solver and a graph-based heuristic algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equation (16) minimizes the objective function 2 which tries to satisfy the preferred mean complexity of each semester. First, the preferred mean complexity of each semester is multiplied by its total assigned credits, t…”

Section: Proposed Optimization Modelmentioning

confidence: 99%

“…• Three objective functions, Equations (15)(16)(17), are replaced with three goal-based constraints Equations (18)(19)(20). • An aggregated objective functions is formulated to minimize total weighted deviations from goals determined in the goal-based constraint Equation (25).…”

Section: Proposed Optimization Modelmentioning

confidence: 99%

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An optimization‐based decision support system for students' personalized long‐term course planning

Shakhsi‐Niaei

Abuei‐Mehrizi

2020

Comp Applic In Engineering

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This paper intends to facilitate academic advising by means of optimal long‐term course planning (LTCP), which is instrumental in students' retention and success. LTCP ensures that students will satisfy the educational requirements without facing unnecessary delays. The proposed decision support system is based on an optimization model which deals with several student preferences, that is, maximum credits per semester, difficulty of courses, graduation semester, and elective courses priorities. The proposed system has been implemented in an industrial engineering department, and several improvements and modifications have been done based on student feedback. The developed system supports two kind of users, that is, educational staff and the students. The courses, their relationships, and regulation parameters are defined/set by educational staff, whereas students determine their own information and preferences, for example, finished courses, graduation target, interest for each elective course, complexity of courses, course load and complexity for each semester, and relative weights of objective functions. Considering theoretical issues, common applications have been integrated to form this system with simple and user‐friendly interfaces.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Optimization Modelmentioning

confidence: 99%

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An optimization‐based decision support system for students' personalized long‐term course planning

Shakhsi‐Niaei

Abuei‐Mehrizi

2020

Comp Applic In Engineering

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“…Based on the student's history of grades and on the performance of the student at each state, a directed structure with the state transitions has been constructed and the courses are recommended such that the grades are always balanced in each state [27]. An optimized course sequence recommendation with prerequisite constraints is solved using multiple integer linear programming algorithms and used structure-based heuristics for reducing the time to a degree [28]. Courses are ranked and optimal course sequences are recommended based on the student population's performance using a rank aggregation framework [29].…”

Section: Related Workmentioning

confidence: 99%

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

Premalatha

Viswanathan

2019

Cybernetics and Information Technologies

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Choice Based Course Selection (CBCS) allows students to select courses based on their preferred sequence. This preference in selection is normally bounded by constraints set by a university like pre-requisite(s), minimum and maximum number of credits registered per semester. Unplanned course sequence selection affects the performance of the students and may prolong the time to complete the degree. Course Difficulty Index (DI) also contributes to the decline in the performance of the students. To overcome these difficulties, we propose a new Subset Sum Approximation Problem (SSAP) aims to distribute courses to each semester with approximately equal difficulty level using Maximum Prerequisite Weightage (MPW) Algorithm, Difficulty Approximation (DA) algorithm and Adaptive Genetic Algorithm (AGA). The three algorithms have been tested using our university academic dataset and DA algorithm outperforms with 98% accuracy than the MPW and AGA algorithm during course distribution.

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“…In the Education field, the collaborative filtering method is observed to be used effectively in the recommendation system [15], [16] in line with the success of the content-based approach [17], [18] and recently the most widely used method is hybrid [19], [20] and context-aware/context-sensitive [21], [22]. However, some researchers also found that rating determination in the recommendation system in the field of Education stated as a cold start [24] In this regard, this study adopts a cognitive theory that describes learning as a business process which involves mental activities occurring in humans as a result of an active interaction with their environment to obtain a change in the form of knowledge, understanding, behavior, skills, values and attitude that are related and important.…”

Section: Introductionmentioning

confidence: 99%

A User-Item Collaborative Filtering System to Predict Online Learning Outcome

Murad¹,

Hassan²,

Wahi³

et al. 2020

Adv. sci. technol. eng. syst. j.

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Education has seen the rapid development of online learning. Many researchers have conducted studies on the use of recommendation systems in online learning. However, until now, several similar studies still focus on the accuracy of the prediction results. Various obstacles were encountered related to changes in the face to face learning process into online learning. This study uses the User-Item Collaborative Filtering method to predict student learning outcomes as a basis for providing recommendations to students. Data on student online learning outcomes were extracted using several methods as a basis for determining and assessing their learning outcomes. The dataset we use is dummy to match the original data. The findings of this study reveal that one of the reinforcing factors that affect student achievement in online learning is the quiz score. The students' high achievement in the quizzes was also balanced by their active involvement during the learning process. Based on the evaluation of the recommendation system, it is known that the gradient boosted tree model is the best model for predicting the final score of student online learning with an accuracy calculated using the highest correlation of 0.7 and the smallest absolute error of 13.0 and root mean square error of 17.9. Based on the results of the evaluation, this study provides recommendations in the form of material links and learning archives that are useful for students to be able to carry out independent learning.

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A Multi-stage Approach to Personalized Course Selection and Scheduling

Cited by 6 publications

References 19 publications

An optimization‐based decision support system for students' personalized long‐term course planning

An optimization‐based decision support system for students' personalized long‐term course planning

Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

A User-Item Collaborative Filtering System to Predict Online Learning Outcome

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