Publication:
Flowchart-based Bayesian Intelligent Tutoring System for computer programming

Simple item page
dc.citedby7
dc.contributor.authorHooshyar D.en_US
dc.contributor.authorAhmad R.B.en_US
dc.contributor.authorFathi M.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorHooshyar M.en_US
dc.contributor.authorid56572940600en_US
dc.contributor.authorid24829264100en_US
dc.contributor.authorid56976006900en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid57189236826en_US
dc.date.accessioned2023-05-29T05:59:39Z
dc.date.available2023-05-29T05:59:39Z
dc.date.issued2015
dc.descriptionBayesian networks; Computer aided instruction; Computer programming; Decision making; Education computing; Flowcharting; Problem solving; Smart sensors; Flowchart development; Intelligent tutoring system; Knowledge level; Learning programming; Novice programmer; Prior knowledge; Problem solving skills; Target audience; Computer systems programmingen_US
dc.description.abstractThere is a misconception of what programming is at the early stages of learning programming for Computer Science (CS) minors. More researches in this field have revealed that the lack of problem-solving skills, which is considered as one of the prominent shortcomings that novices deal with, is exacerbated by language syntax that the novices employ. A Flowchart-based Intelligent Tutoring System (FITS) is proposed in this research aimed at introducing the early stages of learning programming (CS1) to put the record straight. The students who have no prior knowledge of programming are the target audience of this research. In order to support novice programmers in beginning of programming, Bayesian network approach is applied mainly for decision making and to handle uncertainties in knowledge level of students. How to use Bayesian network to take full advantage of it as an inference engine for providing users with various guidance is described in this paper. Therefore, our proposed system provides users with dynamic guidance such as recommend learning goals, recommend options for flowchart development, and generate appropriate reading sequences. Additionally, our proposed system's architecture and its components are elaborated. Our future work is to evaluate the FITS by conducting an experimental study using novices. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7322528
dc.identifier.doi10.1109/ICSSA.2015.7322528
dc.identifier.epage154
dc.identifier.scopus2-s2.0-85009210592
dc.identifier.spage150
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85009210592&doi=10.1109%2fICSSA.2015.7322528&partnerID=40&md5=870814d8b0d7f306f0ac8875950132d0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22216
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2015 International Conference on Smart Sensors and Application, ICSSA 2015
dc.titleFlowchart-based Bayesian Intelligent Tutoring System for computer programmingen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS