A Personalized E-Learning Environment to Promote Student's Conceptual Learning on Basic Computer Programming

Chookaew, Sasithorn; Panjaburee, Patcharin; Wanichsan, Dechawut; Laosinchai, Parames

doi:10.1016/j.sbspro.2014.01.303

Cited by 40 publications

(31 citation statements)

References 5 publications

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“…These results agreed with previous studies showing that the students felt the learning system useful once they received meaningful learning activities according to their actuality of learning (Furo, 2014;Kularbphettong et al, 2015;Lin et al, 2013). While Chookaew et al (2014), Hsu et al (2013), andSong et al (2012) reported that students' personality (e.g. age, gender, and learning preference) could be in consideration when providing the instruction.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Chen (2008) found that personalised e-learning system with generated learning paths according to the incorrect responses could promote learning performance. Chookaew et al (2014) proposed a personalised e-learning environment to enhance students' learning performance on computer programming course. It was found that students who learned with such systems revealed positive effects towards the system in different aspects including usefulness, perceptions, and satisfaction (Kim, 2012;Liaw and Huang, 2013).…”

Section: Mastery Learning and Personalised Learning Support Systemmentioning

confidence: 99%

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A proposal to develop a guided-inquiry mobile learning with a mastery learning mechanism for improving students' learning performance and attitudes in Physics

Wongwatkit

Panjaburee

Srisawasdi

2017

IJMLO

Self Cite

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Abstract:With the benefits of personalised learning environment in providing individual learning guidance and of guided-inquiry approach for driving students to inquire knowledge in procedural way, this study developed a personalised learning support system, which was based on conceptual learning problems, learning styles, and current understanding information, to provide guided-inquiry learning activities for individual students. The effects of the developed system on secondary school students Physics learning performance and learning attitudes were investigated. It was found that the students significantly outperformed on the developed system and their learning attitudes were positive. While, they showed the need of learning Physics in real context by basing on the developed system. In light of the aforesaid, this paper extends the proposed instructional strategy and addresses the application proposal by using the mobile device through authentic Physics laboratory activities. Keywords: mastery learning; mobile learning; personalised learning environment; guided inquiry; science education; online adaptive learning; interactive learning environment; learning style; conceptual learning suggestion; learning diagnostics.Reference to this paper should be made as follows: Wongwatkit, C., Panjaburee, P. and Srisawasdi, N. (2017) 'A proposal to develop a guidedinquiry mobile learning with a mastery learning mechanism for improving students' learning performance and attitudes in Physics', Int. J. Mobile Learning and Organisation, Vol. 11

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Section: Discussionmentioning

confidence: 99%

Section: Mastery Learning and Personalised Learning Support Systemmentioning

confidence: 99%

A proposal to develop a guided-inquiry mobile learning with a mastery learning mechanism for improving students' learning performance and attitudes in Physics

Wongwatkit

Panjaburee

Srisawasdi

2017

IJMLO

Self Cite

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“…Reading and writing programming codes demands learners' prerequisite identifications of both programming functions and grammars to be implemented. Unless learners attain mindful awareness of programming logic, they could not clearly understand basic concepts and their relationships among codes (Chookaew, Panjaburee, Wanichsan, & Laosinchai, 2014). In addition, high variations of learners' familiarity and prior knowledge level in computer programming can also be a determinant that personalized learning modules should be delivered.…”

Section: Personalized Learningmentioning

confidence: 99%

A conceptual framework for teaching computational thinking in personalized OERs

Moon

Lee

et al. 2020

Smart Learn. Environ.

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Interests towards teaching programming skills have risen recently in the realm of computing education. Learning how to program not only enables learners to develop computing applications, but it can also enhance learners' computational thinking (CT) practice. CT refers to learners' ability to approach ill-structured tasks systematically based on algorithmic thinking in computing. Along with growing academic interests towards CT in recent studies, researchers have emphasized the role of teaching programming in facilitating learners' problem-solving skills. Emerging OERs have expanded learners' opportunities to engage in hands-on programming exercises; yet a challenge still remains as to how learners' programming exercises can be tailored to accommodate individual differences in terms of learners' digital literacy skills. There is still a lack of in-depth discussions on how to support learners' personalized learning experiences during programming exercises associated with CT. This study hence proposes a conceptual framework that seeks to consider how to promotelearners' personalized learning experiences and enhance their CT skills in OERs. Through extensive reviewing of literature, this study provides several implications for further research.

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“…MobiSWAP [50] (UIn) Ontology-based (U), (T), (ST.T) -U-learn [36] (UIn) n/a (U) -Benlamri and Zhang [10] (D), (UIn) Ontology-based (U), (T), (P), (ST) Network (bandwidth) Kim and Lee [63] (S) DB (E), (ST.L) GPS, Camera UoLmP [45] (S), (UIn) n/a (U), (T), (E), (ST) GPS E-SoRS [3] (UIn) Ontology-based (U) -Chookaew et al [28] (UIn) n/a (U) -Yin et al [123] (S), (D), (UIn) Markup scheme (XML) (U), (T), (P) GPS SCROLL [74] (S), (D), (UIn) n/a (U), (T), (E), (ST) RFID, GPS, Camera AMDPC [121] (UIn) n/a (U) -Protus 2.0 [112] (UIn) Ontology-based (U) -RLP Adaptation Model [61] (S)(D) n/a (ST) Network MLAS [29] (UIn) Markup schema (U), (T) -Learn-B [101] (UIn) Ontology-based (U), (P) -Gallego et al [41] (S), (UIn) n/a (U), (T), (ST) n/a CAULS [24] (S), (UIn) DB (U), (ST.L) RFID Wu et al [119] (S), (UIn) DB (ST) RFID Alharbi et al [4] (UIn), (D) DB (U) Network Oscar [70] (UIn) n/a (U) -Behaz and Djoudi [9] (UIn) Ontology-based (U) -Despotovic-Zrakic et al [37] (UIn) n/a (U) - [114] (UIn), (S), (D) Ontology-based (U), (T), (E), (P), (ST) GPS IWT [20] (UIn) Ontology-based (U), (T), (P) -Jia et al [60] (UIn) Ontology-based (U) -Wang and Wu [116] (S), (UIn) DB (U) RFID Yaghmaie and…”

Section: System Context Acquisition Context Modeling Context Entitiesmentioning

confidence: 99%

“…Appendix D. Pedagogy in ACALEs [43]: informative (right or wrong without explanation of why), corrective (correction and instruction on how to get the correct answer), explanatory (explanation of why the answer was right or wrong), diagnostic (explanation of why a wrong answer was chosen, and correction of the mistake), point-based (measurement of the answer's accuracy and quality), consequence-based (reaction by changing the system's path of actions), and interactional (provision of corrective feedback based on the learner's utterances, for instance, on pronunciation [35] Computer Architecture Individual learning n/a n/a Chen et al [26] Botanics Individual learning n/a n/a Tarus et al [108] n/a Individual learning n/a n/a SMART [1] n/a Individual learning n/a Summative BCAULS [27] Natural [22] Astronomy Individual learning n/a Summative MobiSWAP [50] Computer science Individual learning Informative Formative U-learn [36] n/a Collaborative learning n/a n/a Benlamri and Zhang [10] Computer Science [C++], Photography Individual learning n/a n/a Kim and Lee [63] English Individual learning n/a n/a UoLmP [45] Business, English Individual learning, Collaborative learning n/a n/a E-SoRS [3] Graduate-level course Collaborative learning n/a n/a Chookaew et al [28] Computer Science [Basic programming] Individual learning n/a n/a Yin et al [123] Workplace Learning Individual learning n/a n/a SCROLL [74] Japanese Individual learning n/a n/a AMDPC [121] Computer Science [Computer Networks] Individual learning n/a n/a Protus 2.0 [112] Computer Science [Java programming] Individual learning Informative Formative RLP Adaptation Model [61] Language learning Individual learning n/a n/a MLAS [29] n/a Individual learning n/a n/a Learn-B [101] Workplace Learning Individual learning n/a n/a Gallego et al [41] n/a Individual learning n/a n/a CAULS [24] Learning in museum Individual learning Informative Formative Wu et al [119] Nursery Individual learning Point-based Formative Alharbi et al [4] Research work, Electrical Engineering Collaborative learning n/a n/a Oscar [70] Computer Science [SQL] Individual l...…”

Section: Rule-basedmentioning

confidence: 99%

A survey of adaptive context-aware learning environments

Hasanov

Laine

Chung

2019

AIS

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Adaptive context-aware learning environments (ACALEs) can detect the learner's context and adapt learning materials to match the context. The support for context-awareness and adaptation is essential in these systems so that they can make learning contextually relevant. Previously, several related surveys have been conducted, but they are either outdated or they do not consider the important aspects of context-awareness, adaptation and pedagogy in the domain of ACALEs. To alleviate this, a comprehensive literature search on ACALEs was first performed. After filtering the results, 53 studies that were published between 2010 and 2018 were analyzed. The highlights of the results are: (i) mobile devices (PDAs, mobile phones, smartphones) are the most common client types, (ii) RFID/NFC are the most common sensors, (iii) ontology is the most common context modeling approach, (iv) context data typically originates from the learner profile or the learner's location, (v) rule-based adaptation is the most used adaptation mechanism, and (vi) informative feedback is the most common feedback type. Additionally, we conducted a trend analysis on technology usage in ACALEs throughout the covered timespan, and proposed a taxonomy of context categories as well as several other taxonomies for describing various aspects of ACALEs. Finally, based on the survey results, directions for future research in the field were given. These results can be of interest to educational technology researchers and to developers of adaptive and context-aware applications.

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A Personalized E-Learning Environment to Promote Student's Conceptual Learning on Basic Computer Programming

Cited by 40 publications

References 5 publications

A proposal to develop a guided-inquiry mobile learning with a mastery learning mechanism for improving students' learning performance and attitudes in Physics

A proposal to develop a guided-inquiry mobile learning with a mastery learning mechanism for improving students' learning performance and attitudes in Physics

A conceptual framework for teaching computational thinking in personalized OERs

A survey of adaptive context-aware learning environments

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