A Genetic Algorithm-Based Personalized Remedial Learning System for Learning Object-Oriented Concepts of Java

Lin, Che‐Chern; Liu, Zi-Cheng; Chang, Chia Ling; Lin, Yu-Wen

doi:10.1109/te.2018.2876663

Cited by 10 publications

(9 citation statements)

References 12 publications

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“…McCall & Kölling, 2019; Ullah et al, 2019;Ureel & Wallace, 2019;Albluwi, 2018;Bhatia, Kohli & Singh, 2018;Lagus et al, 2018;Lin et al, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Wainer & Xavier, 2018;Castro-Wunsch, Ahadi & Petersen, 2017;Ahadi, Hellas & Lister, 2017;Echeverría et al, 2017;Hsiao, Huang & Murphy, 2017;Malliarakis, Satratzemi & Xinogalos, 2016;Scott et al, 2015;Xinogalos, 2015;Allinjawi, Al-Nuaim & Krause, 2014;Omer, Farooq & Abid, 2020 Greater or equal to 4 and less than 5 18Azcona, Hsiao & Smeaton, 2018;Esteero et al, 2018;Gomes & Correia, 2018;King, 2018;Koong et al, 2018;Premchaiswadi, Porouhan & Premchaiswadi, 2018;Dorodchi, Dehbozorgi & Frevert, 2017;España-Boquera et al, 2017;Estey, Keuning & Coady, 2017;Fu et al, 2017;Kumar, 2017;Seanosky et al, 2017;Seeling & Eickholt, 2017;Gomes, Correia & Abreu, 2016;Seeling, 2016;Simkins & Decker, 2016;Ahadi et al, 2015;Ashenafi, Riccardi & Ronchetti, 2015;Carter, Hundhausen & Adesope, 2015;Hilton & Rague, 2015;Janke, Br...…”

Section: Year-wise Publication Trendsmentioning

confidence: 99%

“…Santana, Figueredo & Bittencourt, 2018 assigning weights to the instances that were involved in predictions. The learning approaches were examined, e.g., through different learning patterns (Echeverría et al, 2017), and by applying the genetic algorithms for identification of personalized learning needs (Lin et al, 2018). Gamification was explored as a technique to enhance students' programming abilities (Malliarakis, Satratzemi & Xinogalos, 2016).…”

Section: Xinogalos 2015mentioning

confidence: 99%

“…McCall & Kölling, 2019;Azcona, Hsiao & Smeaton, 2018;Pereira et al, 2020;Chaweewan et al, 2018;Omer, Farooq & Abid, 2020;Ninrutsirikun et al, 2020;Premchaiswadi, Porouhan & Premchaiswadi, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Ahadi, Hellas & Lister, 2017;Ahmad et al, 2017;al-Rifaie, Yee-King & d'Inverno, 2017;Carter, Hundhausen & Adesope, 2017;Castro-Wunsch, Ahadi & Petersen, 2017;Chung & Hsiao, 2020;Funabiki, Ishihara & Kao, 2016;Watson, Li & Godwin, 2014;Ahadi et al, 2015;Ashenafi, Riccardi & Ronchetti, 2015;Carter, Hundhausen & Adesope, 2015 Approach examination Liao et al, 2019;King, 2018;Lagus et al, 2018;Lin et al, 2018;Santana, Figueredo & Bittencourt, 2018;Effenberger, Pelánek & Čechák, 2020;Echeverría et al, 2017;España-Boquera et al, 2017;Iqbal Malik & Coldwell-Neilson, 2017;Seeling & Eickholt, 2017;Malliarakis, Satratzemi & Xinogalos, 2016;Seeling, 2016;Hilton & Rague, 2015;Janke, Brune & Wagner, 2015;Rubio et al, 2014;Rosiene & Rosiene, 2015;Scott et al, 2015;Wood & Keen, 2015;…”

Section: Features Inspectionmentioning

confidence: 99%

“…Types StudiesModelLiao et al, 2019;Bhatia, Kohli & Singh, 2018;Ninrutsirikun et al, 2020;Effenberger, Pelánek & Čechák, 2020;Iqbal Malik & Coldwell- Neilson, 2017;Ashenafi, Riccardi & Ronchetti, 2015; Carter, Hundhausen & Adesope, 2015 Framework Yeomans, Zschaler & Coate, 2019; Chaweewan et al, 2018; Omer, Farooq & Abid, 2020; Premchaiswadi, Porouhan & Premchaiswadi, 2018 Experiment McCall & Kölling, 2019; Ullah et al, 2019; Ureel & Wallace, 2019; Pereira et al, 2020; Esteero et al, 2018; Gomes & Correia, 2018;King, 2018;Lin et al, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Ahadi, Hellas & Lister, 2017;Ahmad et al, 2017;Delev & Gjorgjevikj, 2017;Dorodchi, Dehbozorgi & Frevert, 2017;Echeverría et al, 2017;España-Boquera et al, 2017;Estey, Keuning & Coady, 2017;Chung & Hsiao, 2020;Fu et al, 2017;Hsiao, Huang & Murphy, 2017;Kumar, 2017;Seanosky et al, 2017;Berges et al…”

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confidence: 99%

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Introductory programming course: review and future implications

Omer

Farooq

Abid

2021

PeerJ Computer Science

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The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interesting dimensions of IPC, such as collaborative learning, cognitive assessments, and performance predictions. In addition to reviewing the recent advancements in IPC, this study proposes a new taxonomy of IPC research dimensions. Furthermore, based on the successful practices that are listed in the literature, some useful guidelines and advices for instructors have also been reported in this article. Lastly, this review presents some pertinent open research issues to highlight the future dimensions for IPC researchers.

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Section: Year-wise Publication Trendsmentioning

confidence: 99%

Section: Xinogalos 2015mentioning

confidence: 99%

Section: Features Inspectionmentioning

confidence: 99%

mentioning

confidence: 99%

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Introductory programming course: review and future implications

Omer

Farooq

Abid

2021

PeerJ Computer Science

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“…The flow of remedial learning is an assessment-learning-reassessment. Many methods are used in remedial learning such as tailoring personalized learning materials according to each student's strengths and weaknesses (Lin et al, 2018), Reciprocal Teaching.…”

Section: Integrationmentioning

confidence: 99%

Immersive Intelligent Tutoring System for Remedial Learning Using Virtual Learning Environment

Rasim

Rosmansyah

Langi

et al. 2021

Indonesian J. Sci. Technol

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Intelligent Tutoring System (ITS) has been widely used in supporting personal learning. However, there is an aspects that have not become focus in ITS, namely immersive. This research proposes an Immersive Intelligent Tutoring (IIT) model based on Bayesian Knowledge Tracing (BKT) for determining the learner’s characteristics and learning content delivery strategies using genetic algorithms. The model uses remedial learning with a faded worked-out example. This study uses a 3-Dimensional Virtual Learning Environment (3DMUVLE) that implements immersive features to increase intrinsic motivation. This model was built using a client / server architecture. The server side component uses the MOODLE, the client side component uses OpenSim and its viewers, and the middleware component uses the Simulation Linked Object Oriented Dynamic Learning Environment (SLOODLE). Model testing is performed on user acceptance using a combination of Technology Acceptance Model (TAM) and Hedonic-Motivation System Adoption Model (HMSAM) and the impact of the model in learning using statistical test. The results showed 83% of the learners felt happy with the learning. Meanwhile, the evaluation of the impact on learning outcomes shows that the use of this model is significantly different from traditional learning.

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