A Review of Methods To Measure Affective Domain in Learning

Syaiful, Lusiana; Ismail, Muhammad Hussain; Aziz, Zalilah Abd

doi:10.1109/iscaie.2019.8743903

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…The second through fifth variables in the psychomotor domain, based on the instructional design and learning requirement of the hands-on course and previous studies (Edward, 2002;Krivickas and Krivickas, 2007), were, respectively, defined as self-learning (SELF), the utilization of learning resources (UTI), collaborative learning (COL), and expression and communication (EXP). The sixth variable, attitude and affectivity (ATT; Syaiful et al, 2019), was based on the affective domain and "describe[s] changes in interest, attitudes, and values, and the development of appreciations and adequate adjustment" (Bloom et al, 1956, p. 7).…”

Section: Instrumentsmentioning

confidence: 99%

Innovative Pedagogy and Design-Based Research on Flipped Learning in Higher Education

Zhao

2021

Front. Psychol.

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In order for higher education to provide students with up-to-date knowledge and relevant skillsets for their continued learning, it needs to keep pace with innovative pedagogy and cognitive sciences to ensure inclusive and equitable quality education for all. An adequate implementation of flipped learning, which can offer undergraduates education that is appropriate in a knowledge-based society, requires moving from traditional educational models to innovative pedagogy integrated with a playful learning environment (PLE) supported by information and communications technologies (ICTs). In this paper, based on the design-based research, a task-driven instructional approach in the flipped classroom (TDIAFC) was designed and implemented for two groups of participants in an undergraduate hands-on making course in a PLE. One group consisting of 81 students as the experimental group (EG) received flipped learning instruction, and another group of 79 students as the control group (CG) received lecture-centered instruction. The EG students experienced a three-round study, with results from the first round informing the customized design of the second round and the second round informing the third round. The experimental results demonstrated that students in the EG got higher scores of summative tests and final scores than those in the CG. In particular, students’ learning performance in three domains (i.e., cognitive, affective, and psychomotor) differ significantly between the two groups.

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Section: Instrumentsmentioning

confidence: 99%

Innovative Pedagogy and Design-Based Research on Flipped Learning in Higher Education

Zhao

2021

Front. Psychol.

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“…Deep learning techniques like long short-term memory networks (LSTM) and convolutional neural networks (CNN) can also be used for classification and prediction, and another important function of it is the automatic extraction of data features. Fuzzy Logic, which has the ability to identify, represent, manipulate, interpret and exploit data and information that are vague and lack certainty, was applied to detecting academic emotions (Syaiful et al, 2019). Clustering is another technique that aggregates similar examples together to distinguish different sets.…”

Section: Summarization Of Literature Based On Larmmentioning

confidence: 99%

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Liu

Ren

Kong

et al. 2022

Journal of Educational Computing Research

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Wearable devices are an emerging technological tool in the field of learning analytics. With the help of wearable technologies, an increasing number of scholars have a strong interest in studying the associations between student data and learning outcomes in different learning environments. This systematic review examines 120 articles published between 2011 and 2021, exploring current research on learning analytics based on wearable devices in detail from both descriptive and content analysis. The descriptive analysis reviewed the included literature in five dimensions: publication times of the reviewed literature, wearable devices and data types used in studies, stakeholders, objectives, and methods involved in the analysis procedure. The content analysis aims to examine the literature covered in terms of three categorical domains of educational objectives: cognitive, affective, and behavioral, to investigate the practical applications and potential issues of learning analytics based on wearable devices. After that, based on the overall research content of the reviewed literature, a framework for learning analytics based on wearable devices is present, and its application process is summarized and analyzed for the reference of related researchers. At last, we summarize the limitations of existing studies and present several recommendations to further promote research and development in this field.

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“…Reference [3] presents a review of research efforts related to automated assessment for affective domain or related mental state. Authors are of the view that affective domain is linked to how 'people react emotionally' and to the ability of people for feeling 'another living thing's pain or joy'.…”

Section: A Research Efforts Related To Affective Domainmentioning

confidence: 99%

Employing Systems Thinking to Assess Effective Assessment of the Affective Domain

Dastgeer¹,

Sadaf²,

Umer³

et al. 2022

Preprint

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A Review of Methods To Measure Affective Domain in Learning

Cited by 7 publications

References 15 publications

Innovative Pedagogy and Design-Based Research on Flipped Learning in Higher Education

Innovative Pedagogy and Design-Based Research on Flipped Learning in Higher Education

Learning Analytics Based on Wearable Devices: A Systematic Literature Review From 2011 to 2021

Employing Systems Thinking to Assess Effective Assessment of the Affective Domain

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