Factors Analysis of Affective Assessment in Electronic Education
DOI:
https://doi.org/10.55766/HRLY3160Keywords:
Affective Assessment, e-Education, Massive Open Online Courses, Exploratory Factor Analysis, Confirmatory Factor AnalysisAbstract
The purpose of this research is to analyze factors of affective assessment in electronic education. The data were collected from 120 teachers who participate in the thai cyber university development project for thailand massive open online course (Thai-MOOC). The data were collected using 5-point likert scale questionnaire. The data were analyzed using the Exploratory Factor Analysis (EFA) and the second order Confirmatory Factor Analysis (CFA). The results of the EFA consisted of four latent variables which are grouped as responsibility, attention to teaching materials, online classroom participation and honest. The results of the second order CFA implies that the developed model was consistent with the empirical data (chi-square = 55.76, degrees of freedom = 42, p-value 0.075 = CFI = 0.989, TLI = 0.983, RMSEA = 0.052, SRMR = 0.065).
References
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Carmona, C. J., González, P., Jesus, M. J. D., & Ventura, S. (2011). Subgroup Discovery in An e-Learning Usage Study Based on Moodle. International Conference on Next Generation Web Services Practices 2011 7th, Salamanca, Spain.
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Charoentum, B. (1997). Construction of an Affective Domain observation From in Chemistry for Mathayom Suksa 5 Students. (In Thai). M.S. thesis of Education, Chingmai University, Chingmai
Comendador, B. E. V., Rabago, L. W., & Tanguilig, B. T. (2016). An Educational Model Based on Knowledge Discovery in Databases (KDD) to Predict Learner's Behavior Using Classification Techniques. International Conference on Signal Processing, Communications and Computing 2016, Hong Kong, China
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Kularbphettong, K., & Tongsiri, C. (2012). Mining Educational Data to Analyze the Student Motivation Behavior. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 6(8): 1036-1040.
Manne, S., Yelisetti, S., Kakarla, M., & Fatima, S. (2014). Mining VRSEC Student Learning Behaviour in Moodle System Using Datamining Techniques. International Conference on Computing and Communication Technologies. Hyderabad, India.
Meechai, M. (2001). Relationships of Some Factors of the Affective Domain and Mathematics Leaning Achievement of Prathomsuksa 6 Students under the Provincial Office of Primary Education, Amnatchareon. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Meechai, P. (2005). Relationships among Some Affective Domain, Mathematics Learning Behaviors and Mathematics Teaching Behaviors and Mathematics Problem Solving for Pratomsuksa 6 Students in Yasothon Province. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Mustapha, A., Samsudin, N. A., Arbaiy, N., Mohammed, R., & Hamid, I. R. (2016). Generic Assessment Rubrics for Computer Programming Courses. Turkish Online Journal of Educational Technology-TOJET. 15(1): 53-68.
Namburi, S. (2006). Approaches to Ethical Assessment Supporting Learner’s Achievement through e-Learning System. (In Thai) Journal of Yala Rajabhat University. 1(2): 138-155.
Namburi, S. (2009). Development of System Supporting Ethical Behavior Assessment and Improvement for Learner’s Responsibility in Electronic Learning Environment. (In Thai). Doctor of Philosophy in Computer Education, King Mongkut’s University of Technology North Bangkok, Bangkok.
Namburi, S., Sureerattanan, N., & Thianthong, M. (2010). Development of New Activity Module for Moodle Plug-in. Technical Education Journal King Mongkut’s University of Technology North Bangkok. 1(1): 66-75.
Navawongsathien, A. (2009). Assuring Quality in e-Learning: The Challenging of No-boundary Learning. (In Thai) Journal of Education Burapha University. 20(3): 47-62.
Phongsiri, W., Jansopha, U., & Puangpronpitag, S. (2014). e-learning Log Analysis for Behavioral Scoring. (In Thai). Journal of Science and Technology Mahasarakham University Special issue 10th. 10: 121-131.
Promwong, B. (1999). The Construction of an Affective Domain Test in Buddhism (S 0113) Course for Junior Secondary School Curriculum, B.E. 1978 (Revised Edition 1990). (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Ramalingam, M., Kasilingam, G., & Chinnavan, E. (2014). Assessment of Learning Domains to Improve Student's Learning in Higher Education. Journal of Young Pharmacists. 6(1): 27-33.
Romero, C., González, P., Ventura, S., Jesus, M. J. D., & Herrera, F. (2009). Evolutionary Algorithms for Subgroup Discovery in e-Learning: A Practical Application Using Moodle Data. Expert Systems with Applications. 36(2): 1632-1644.
Ruangrit, N. (2015). States and Needs of Online Learning Resources on MOOCs for Education. Veridian E-Journal, Silpakorn University. 8(2): 124-140.
Saripun, U. (2006). Relationships Between Affective Domain Factors and Mathematics Learning Achievement of Prothom Suksa 6 Students in the Ratthanakosin School Group under the Bangkok Metropolitan Administation. (In Thai). M.S. thesis of Education, Ramkhamhaeng University, Bangkok.
Savickiene, I. (2010). Conception of Learning Outcomes in the Bloom's Taxonomy Affective Domain. Quality of Higher Education. 7: 37-59.
Sikkhaman, K. (2011). A Study of Learning Achievement in Business English Communication Course by Using e-Learning Teaching (In Thai). Bangkok.
Sincharu, T. (2012). Research and statistical analysis with SPSS and AMOS. (In Thai) (13ed.). Bangkok: Business R & D.
Sisovic, S., Matetic, M., & Bakaric, M. B. (2016). Clustering of Imbalanced Moodle Data for Early Alert of Student Failure. International Symposium on Applied Machine Intelligence and Informatics 14th, Herlany, Slovakia.
Sulaimany, S., Maghsoudi, B., & Amiri, A. (2011). Improving the Performance of e-Learning Systems Using Extracted Web Server Log File Suggestions. . International Journal of Information & Communication Technology Research. 3(1): 67-72.
Thipsungnoen, S. (2004). Creation and Validation of the Efficiency of an e-Learning on "Introduction of Computer and Internet" for the Students in Secondary Schools, Department of Education. (In Thai). M.S. thesis of Industrial Education, King Mongkut's Institute of Technology North Bangkok Bangkok.
Thitithananon, T. (2007). Principles of Measurement and Evaluation of Education. (In Thai). Nakhonratchasrima: Nakhon Ratchasima Rajabhat University.
Tirasirawet, W. (2003). Relationships among Teaching Behaviors, Leaning Behaviors and Affective Bahaviors and Mathematics Achievement of Mattayomsuksa 3 Students in Maha Sarakham Province. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Wanichbancha, K. (2013). Structural equation analysis (SEM) with AMOS. (In Thai). Bangkok: Sam Lada.
Yamane, T. (1973). Statistics: an introduction analysis: Harper & Row.
Ahmad, N. B., Ishak, M. K., Alias, U. F., & Mohamad, N. (2015). An Approach for E-Learning Data Analytics using SOM Clustering. International Journal of Advances in Soft Computing & Its Applications. 7(3): 94-111.
Alias, U. F., Ahmad, N. B., & Hasan, S. (2015). Student Behavior Analysis using Self-organizing Map Clustering Technique. ARPN Journal of Engineering and Applied Sciences. 10(23): 17987-17995.
Ardseenark, T. (2005). A Comparative of Learning Achicvement between e-Learning method and Conventional method on Information Technology for Learning, Rajabhat Institute Curriculum B.E. 1999. (In Thai) M.S. thesis of Industrial Education, King Mongkut's Institute of Technology Noth Bangkok, Bangkok.
Bloom, B. S., Krathwohl, D. R., & Masia, B. B. (1956). Taxonomy of Educational Objectives : the Classification of Educational Goals. New York: David McKay Company.
Bovo, A., Sanchez, S., Héguy, O., & Duthen, Y. (2013). Clustering Moodle Data as A Tool for Profiling Students. International Conference on e-Learning and e-Technologies in Education (ICEEE), 2013 Lodz, Poland
Carmona, C. J., González, P., Jesus, M. J. D., & Ventura, S. (2011). Subgroup Discovery in An e-Learning Usage Study Based on Moodle. International Conference on Next Generation Web Services Practices 2011 7th, Salamanca, Spain.
Chaipayom, S. (2007). The Causal Relationship Study of Affective Domain InFluencing on Numerical Ability of Junior High School Students, in the Educational Area 1, Phetchaburi Province. (In Thai). M.S. thesis of Education, Srinakharinwirot University, Bangkok.
Chamchumrut, D. (2007). The Study of Correlation among Knowledge, Skill, and Attitudes of Vocational Education Program in Auto-Mechanic, Division of Technical Colleges at the Eastern Region. (In Thai). M.S. thesis of Science in Industrial Education, King Mongkut's University of Technology Thonburi, Bangkok.
Chankub, P. (1988). Factor of Affective Domain Related to the Academic Achievement of Pratomsuksa Six Students in Songkhla Province. (In Thai). M.S. thesis of Education, Prince of Songkla University, Songkla.
Charoentum, B. (1997). Construction of an Affective Domain observation From in Chemistry for Mathayom Suksa 5 Students. (In Thai). M.S. thesis of Education, Chingmai University, Chingmai
Comendador, B. E. V., Rabago, L. W., & Tanguilig, B. T. (2016). An Educational Model Based on Knowledge Discovery in Databases (KDD) to Predict Learner's Behavior Using Classification Techniques. International Conference on Signal Processing, Communications and Computing 2016, Hong Kong, China
Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS. IEEE Transactions on Learning Technologies. 10(1): 17-29.
Glass, L. W. (1970). Assessment of Affective Outcomes of Instruction With High School Sophomore Biology Students and Teachers. Annual Meting of the National Association for Research in ScienceTeaching, Minneapolis Minnesota.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis : A Global Perspective. Upper Saddle River: Pearson Education.
Harvard University, & Massachusetts Institute of Technology. (2016). MOOCs [On-line] Available: http://mooc.org
Hoffmann, M. H. W. (2008). Using Bloom’s Taxonomy of Learning to Make Engineering Courses Comparable. EAEEIE Annual Conference 19th Tallinn. Estonia.
Krathwohl, D. R., Bloom, B. S., & Masia, B. B. (1964). Taxonomy of Educational Objectives, Handbook II: Affective Domain. New York: David McKay Company.
Kularbphettong, K., & Tongsiri, C. (2012). Mining Educational Data to Analyze the Student Motivation Behavior. International Journal of Computer, Electrical, Automation, Control and Information Engineering. 6(8): 1036-1040.
Manne, S., Yelisetti, S., Kakarla, M., & Fatima, S. (2014). Mining VRSEC Student Learning Behaviour in Moodle System Using Datamining Techniques. International Conference on Computing and Communication Technologies. Hyderabad, India.
Meechai, M. (2001). Relationships of Some Factors of the Affective Domain and Mathematics Leaning Achievement of Prathomsuksa 6 Students under the Provincial Office of Primary Education, Amnatchareon. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Meechai, P. (2005). Relationships among Some Affective Domain, Mathematics Learning Behaviors and Mathematics Teaching Behaviors and Mathematics Problem Solving for Pratomsuksa 6 Students in Yasothon Province. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Mustapha, A., Samsudin, N. A., Arbaiy, N., Mohammed, R., & Hamid, I. R. (2016). Generic Assessment Rubrics for Computer Programming Courses. Turkish Online Journal of Educational Technology-TOJET. 15(1): 53-68.
Namburi, S. (2006). Approaches to Ethical Assessment Supporting Learner’s Achievement through e-Learning System. (In Thai) Journal of Yala Rajabhat University. 1(2): 138-155.
Namburi, S. (2009). Development of System Supporting Ethical Behavior Assessment and Improvement for Learner’s Responsibility in Electronic Learning Environment. (In Thai). Doctor of Philosophy in Computer Education, King Mongkut’s University of Technology North Bangkok, Bangkok.
Namburi, S., Sureerattanan, N., & Thianthong, M. (2010). Development of New Activity Module for Moodle Plug-in. Technical Education Journal King Mongkut’s University of Technology North Bangkok. 1(1): 66-75.
Navawongsathien, A. (2009). Assuring Quality in e-Learning: The Challenging of No-boundary Learning. (In Thai) Journal of Education Burapha University. 20(3): 47-62.
Phongsiri, W., Jansopha, U., & Puangpronpitag, S. (2014). e-learning Log Analysis for Behavioral Scoring. (In Thai). Journal of Science and Technology Mahasarakham University Special issue 10th. 10: 121-131.
Promwong, B. (1999). The Construction of an Affective Domain Test in Buddhism (S 0113) Course for Junior Secondary School Curriculum, B.E. 1978 (Revised Edition 1990). (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Ramalingam, M., Kasilingam, G., & Chinnavan, E. (2014). Assessment of Learning Domains to Improve Student's Learning in Higher Education. Journal of Young Pharmacists. 6(1): 27-33.
Romero, C., González, P., Ventura, S., Jesus, M. J. D., & Herrera, F. (2009). Evolutionary Algorithms for Subgroup Discovery in e-Learning: A Practical Application Using Moodle Data. Expert Systems with Applications. 36(2): 1632-1644.
Ruangrit, N. (2015). States and Needs of Online Learning Resources on MOOCs for Education. Veridian E-Journal, Silpakorn University. 8(2): 124-140.
Saripun, U. (2006). Relationships Between Affective Domain Factors and Mathematics Learning Achievement of Prothom Suksa 6 Students in the Ratthanakosin School Group under the Bangkok Metropolitan Administation. (In Thai). M.S. thesis of Education, Ramkhamhaeng University, Bangkok.
Savickiene, I. (2010). Conception of Learning Outcomes in the Bloom's Taxonomy Affective Domain. Quality of Higher Education. 7: 37-59.
Sikkhaman, K. (2011). A Study of Learning Achievement in Business English Communication Course by Using e-Learning Teaching (In Thai). Bangkok.
Sincharu, T. (2012). Research and statistical analysis with SPSS and AMOS. (In Thai) (13ed.). Bangkok: Business R & D.
Sisovic, S., Matetic, M., & Bakaric, M. B. (2016). Clustering of Imbalanced Moodle Data for Early Alert of Student Failure. International Symposium on Applied Machine Intelligence and Informatics 14th, Herlany, Slovakia.
Sulaimany, S., Maghsoudi, B., & Amiri, A. (2011). Improving the Performance of e-Learning Systems Using Extracted Web Server Log File Suggestions. . International Journal of Information & Communication Technology Research. 3(1): 67-72.
Thipsungnoen, S. (2004). Creation and Validation of the Efficiency of an e-Learning on "Introduction of Computer and Internet" for the Students in Secondary Schools, Department of Education. (In Thai). M.S. thesis of Industrial Education, King Mongkut's Institute of Technology North Bangkok Bangkok.
Thitithananon, T. (2007). Principles of Measurement and Evaluation of Education. (In Thai). Nakhonratchasrima: Nakhon Ratchasima Rajabhat University.
Tirasirawet, W. (2003). Relationships among Teaching Behaviors, Leaning Behaviors and Affective Bahaviors and Mathematics Achievement of Mattayomsuksa 3 Students in Maha Sarakham Province. (In Thai). M.S. thesis of Education, Mahasarakham University, Mahasarakham.
Wanichbancha, K. (2013). Structural equation analysis (SEM) with AMOS. (In Thai). Bangkok: Sam Lada.
Yamane, T. (1973). Statistics: an introduction analysis: Harper & Row.
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