Characteristics of Students’ Cognitive Conflict in Solving a Problem Based on Information Processing Theory

Pratiwi, Enditiyas; Nusantara, Toto; Susiswo, Susiswo; Muksar, Makbul; Subanji, Subanji

doi:10.26803/ijlter.18.2.6

Cited by 16 publications

(14 citation statements)

References 9 publications

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“…Some other researchers have examined the role of cognitive conflict in improving spatial visualization based on student ability, sources of students' errors, and misconception in algebra (Mulungye et al, 2016;Susilawati et al, 2017). Others have analyzed the characteristics of students' cognitive conflict when solving problems based on information processing theory (Pratiwi et al, 2019). In addition, others have examined the effects of a mathematics cognitive acceleration program on student achievement and motivation (Finau et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Manifestations and meanings of cognitive conflict among mathematics students in Embu, Kenya

Ngicho¹,

Karuku²,

King’endo³

2020

Educ. Res. Rev.

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Section: Literature Reviewmentioning

confidence: 99%

Manifestations and meanings of cognitive conflict among mathematics students in Embu, Kenya

Ngicho¹,

Karuku²,

King’endo³

2020

Educ. Res. Rev.

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“…This is valid on theoretical and practical levels, e.g. information processing theory combines the behaviourist and cognitive theories (Pratiwi et al , 2019). In this research, a theory refers to a comprehensive, coherent and internally consistent system of ideas about a set of phenomena (Knowles et al , 2015).…”

Section: Pedagogy Of Learningmentioning

confidence: 99%

A review of learning theories and models underpinning technology-enhanced learning artefacts

Hammad

Khan

Safieddine

et al. 2020

WJSTSD

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PurposeVarious technology-enhanced learning software and tools exist where technology becomes the main driver for these developments at the expense of pedagogy. The literature reveals the missing balance between technology and pedagogy in the continuously evolving technology-enhanced learning domain. Consequently, e-learners struggle to realise the pedagogical value of such e-learning artefacts. This paper aims to understand the different pedagogical theories, models and frameworks underpinning current technology-enhanced learning artefacts to pave the way for designing more effective e-learning artefacts.Design/methodology/approachTo achieve this goal, a review is conducted to survey the most influential pedagogical theories, models and frameworks. To carry out this review, five major bibliographic databases have been searched, which has led to identifying a large number of articles. The authors selected 34 of them for further analysis based on their relevance to our research scope. The authors critically analysed the selected sources qualitatively to identify the most dominant learning theories, classify them and map them onto the key characteristics, criticism, approaches, models and e-learning artefacts.FindingsThe authors highlighted the significance of pedagogies underpinning e-learning artefacts. Furthermore, the authors presented the common and special aspects of each theory to support our claim, which is developing a hybrid pedagogical approach. Such a hybrid approach remains a necessity to effectively guide learners and allow them to achieve their learning outcomes using e-learning artefacts.Originality/valueThe authors found that different pedagogical approaches complement rather than compete with each other. This affirms our recommended approach to adopt a hybrid approach for learning to meet learners' requirements. The authors also found that a substantive consideration for context is inevitable to test our evolving understanding of pedagogy.

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“…Students can also use informal strategies to solve problems, and can evaluate each other's thinking results. With understanding, students can develop reasoning skills, using evidence and logic (Pratiwi, Nusantara, Susiswo, Muksar, & Subanji, 2019).…”

Section: Introductionmentioning

confidence: 99%

Understanding the Concept of Intelligent Operations Through ELPSA Learning when Learning From Home

Ramuni

2021

edv

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This Scientific Review in the form of best practice Learning the ELPSA (Experience, Language, Picture, Symbol, Application) model examines learning as an active process where students construct their own way of understanding things through individual thought processes and social interactions with others in BDR activities. In this BDR activity, LKPD is used. This Scientific Review (Best Practice) activity aims to improve understanding of the concept of integer operations through ELPSA learning for class VIIA students of SMPN 5 Sanggau. This activity is carried out in the 2021/2022 school year, to be precise in July. The success criteria set by the researchers are student activities during the BDR learning process in the good category, at least 80% of the number of students can reach the KKM which is 70. The results of this scientific review show that students' understanding of learning integer operations through ELPSA learning when BDR has been achieved by the success criteria shown in the post test results. In more detail, the percentage of achievement of the success criteria is as follows: 1) student activities during the learning process are in the good category, 2) There is an increase in the percentage of students who have reached the KKM by 31.25% from pre-test 56.25% to 87.5% in post test. From the results of the study, it can be concluded that ELPSA learning is effective in increasing the understanding of the concept of integer operations during BDR.

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Characteristics of Students’ Cognitive Conflict in Solving a Problem Based on Information Processing Theory

Cited by 16 publications

References 9 publications

Manifestations and meanings of cognitive conflict among mathematics students in Embu, Kenya

Manifestations and meanings of cognitive conflict among mathematics students in Embu, Kenya

A review of learning theories and models underpinning technology-enhanced learning artefacts

Understanding the Concept of Intelligent Operations Through ELPSA Learning when Learning From Home

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