A Career Track Recommender System for Senior High School Students using Fuzzy Logic

B, Marvee Cheska

doi:10.30534/ijatcse/2019/97852019

Cited by 4 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When it comes to giving importance to students' scores, this study is anchored to [31], [32] about the type of assessment system being used in outcomes-based [31]. This is also supported by [33], especially on higher education [34], which is preparatory for each individual to the real-working world [14], [33].…”

Section: Methodology 31 Processmentioning

confidence: 99%

Algorithmic Analytics for Outcomes-based Tertiary Education Performance Assessment

Jadraque¹

2020

IJATCSE

View full text Add to dashboard Cite

Global educational institutions are continuously working to enhance the needed curricula to answer the demands of state and business enterprise. They are now working with Outcomes-based Education (OBE), which denies the traditional way of education to students. Instead, it makes students show that they are capable of doing the required learning outcomes. The quest for learning the correct assessment for education while under the era of OBE is still an on-going call. With the help of Educational Data Mining, a real-world dataset of 549 were trained from the student record. The study intends to identify the probability of the student to pass practical evaluation following OBE methodology, using selected WEKA-based classifiers, namely Naive Bayes, C4.5, and Random Forest. The said classifiers revealed promising accuracy of 78.10% for Naïve Bayes, 93.07% for C4.5, and 95.99% for Random Forest. Hence, the EDM's future is really for further study, which can be implemented in the fields of entertainment, industry, medicine, and many others because of massive raw databases.

show abstract

Section: Methodology 31 Processmentioning

confidence: 99%

Algorithmic Analytics for Outcomes-based Tertiary Education Performance Assessment

Jadraque¹

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

“…An example of knowledge-based recommender system is developed by [2] which is a mobile application of dietary nutrition recommendation service for obese management. In [3], a fuzzy-based recommender system is developed to address the issue of uncertainty in choosing the right career track by senior high school students.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

Azmi¹

2020

IJATCSE

View full text Add to dashboard Cite

A recommender system is an information filtering system that helps users select items that most match their preferences from a vast amount of information available. It has been widely applied in many domains such as e-commerce, healthcare, entertainment and so on. Currently, there are some efforts have been done for recommending interventions to improve elderly well-being in different aspects of successful ageing. However, the recommendations are focused on only a single aspect of successful ageing such as nutrition or health. There are many aspects of successful ageing that should be considered when given interventions to improve elderly wellbeing namely socialization, health, physical, cognitive, nutrition, spirituality and environment. This paper aims to propose a Hybrid Knowledge-Based and Collaborative Filtering (KBCF) recommendation model to recommend interventions based on multiaspects of successful ageing in order to improve the elderly wellbeing. The Knowledge-based (KB) recommendations are generated by consulting a knowledge base which is developed based on knowledge provided by the domain experts. The Collaborative Filtering (CF) approach is applied to find similar users based on elderly profiles generated from the result of assessments done to the elderly. The results of the KB recommendations and the CF recommendations are integrated and ranked to select the final recommendations. The result of experiments conducted using precision, recall and F1 measure shows that the proposed KBCF model outperforms the baseline models which are Basic Search, Collaborative Filtering and Knowledge-based. This result demonstrates the proposed KBCF model provides more accurate and meaningful intervention recommendations for improving the elderly wellbeing in multiaspects of successful ageing.

show abstract

“…Recommendation systems are very useful and also widely used in the field of education, because they facilitate several tasks, and they increase the quality of the service and also they reduce the processing time. For example, we find [7] which created a system of recommendation for the follow-up of the students, or for the prediction of the performances of the students [8], and also for the prediction of the admission of the students [9]. Researchers in [10] have used ontology for resource recommendation, and also [11] have used ontology to create a course recommendation system for students.…”

Section: Introductionmentioning

confidence: 99%

Student Orientation Recommender System using TOPSIS and AHP

Ouatik,

Erritali,

Ouatik

et al. 2022

JIOS

View full text Add to dashboard Cite

The process of traditional school guidance is carried out by specialists. They make astudy of the student files based on the marks of the first year and the second year ofthe baccalaureate. Considering the progressive number of students and also the lack oftime to make the decision, as the selection of the specialty has a great effect on theacademic course of the students, we have realized a system of specialtyrecommendation, to computerize the orientation process and save time. But the majorproblem is that the students do not care about this process and pay no attention to itdespite its importance. As well as the software which makes the orientation ischargeable.To order these specialties to take the best specialty. We have arrived at aproblem of multi-criteria which makes it impossible to make a decision with thesecriteria. Because these criteria do not have the same importance and also are notcompatible, as there are criteria that must be maximum and other criteria must beminimal. To solve this problem, two systems of orientation and academicreorientation of students have been implemented. In both systems, the SMOTEmethod has been used to balance the learning data in the preprocessing phase. Then inthe treatment phase, we sorted the specialties using in the first system, a hybridizationof TOPSIS method and the information gain to find the weights of the criteria used,and in the second system, we used a hybridization of the AHP method andinformation gain. The results obtained indicate that before balancing data using the SMOTE method, the total accuracy of TOPSIS (84.20%) is higher than the total accuracy of AHP (83.71%). After applying balancing data using the SMOTE method,the total accuracy has increased. The total accuracy of TOPSIS (91.35%) is alsohigher than the total accuracy of AHP (90.83%). For the complexity of the twomethods, it is related to the number of criteria and the number of alternatives. If thenumber of criteria is more than 10 criteria, the complexity of TOPSIS is less than thecomplexity of AHP, and vice versa. The complexity of the two methods also dependson the number of alternatives, if the number of alternatives exceeds 10, the TOPSISmethod becomes more complex than the AHP method. In general, the system basedon TOPSIS method and the information gain is more precise than the system based onthe AHP method and the information gain. But the complexity of the AHP method isless than the complexity of the TOPSIS method.

show abstract

A Career Track Recommender System for Senior High School Students using Fuzzy Logic

Cited by 4 publications

References 34 publications

Algorithmic Analytics for Outcomes-based Tertiary Education Performance Assessment

Algorithmic Analytics for Outcomes-based Tertiary Education Performance Assessment

A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

Student Orientation Recommender System using TOPSIS and AHP

Contact Info

Product

Resources

About