Assessment of readiness for learning and academic success on computer assisted learning: A study on computer integrated manufacturing with lathe

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Betül Erdoğdu Åžakar
İnci Zaim Gökbay
Adem Karahoca

Abstract

With the improvements in technology, computers are being used for assistance in learning and teaching in different fields. Therefore, Computer Assisted Learning is used for our study to teach operating computer numerical controlled lathe machine in computer integrated manufacturing lab. Our education is planned in major phases: traditional teaching and computer assisted learning. Before the education students took a questionnaire with different subscales to determine the level of readiness for learning. And after the education they took a test on operating the lathe machine to measure the success of the education. Then the students were asked to design a project. This education is a comprehensive one and it is not always possible to make all students use the lathe machine since there can be some issues on safety, time management and waste of products. So with this study it is intended to determine the effect of readiness for learning and academic success on the students and give this education accordingly. A dichotomous classification problem is constructed with the subscales of readiness for learning questionnaire and academic success as features and lathe test result as class labels. Consequently, a model that can determine whether a student would be successful in learning to operate the lathe machine with the subscales of the readiness for learning questionnaire (motivation, health – nutrition, learning, planned working, efficient reading, writing, listening, note taking, attending a course, preparation and attending an exam) and GPA is constructed with Support Vector Machines and leave-one-out cross validation.

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How to Cite
Åžakar, B. E., Gökbay, İnci Z., & Karahoca, A. (2016). Assessment of readiness for learning and academic success on computer assisted learning: A study on computer integrated manufacturing with lathe. Global Journal of Information Technology: Emerging Technologies, 6(1), 94–106. https://doi.org/10.18844/gjit.v6i1.396
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References

AbuSeileek, A. F. (2012). The effect of computer-assisted cooperative learning methods and group size on the EFL learners’ achievement in communication skills. Computers & Education , 58, 231–239.

Alpaydin, E. (2010). Introduction to Machine Learning. London: The MIT Press.

Ayotola, A., & Adedeji, T. (2009). The relationship between gender, age, mental ability, anxiety, mathematics self-efficacy and achievement in mathematics. Cypriot Journal of Educational Sciences , 4, 113-124.

Ayres, R. U. (1991). Computer Integrated Manufacturing, Volume 1: Revoluton in Progress. London: Chapman & Hall.

Bagheri, M., Ali, W. Z., Abdullah, M. C., & Daud, S. M. (2013). Project-based learning as a facilitator to promote students’ technology competencies. World Journal on Educational Technology , 5, 207-214.

Bausmith, J. M., & Barry, C. (2011). Revisiting Professional Learning Communities to Increase College Readiness: The Importance of Pedagogical Content Knowledge. Education & Educational Research , 40 (4), 175-178.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Cambridge: Springer.

Blake, C., & Scanlon, E. (2007). Reconsidering simulations in science education at a distance: features of effective use. Journal of Computer Assisted Learning , 23 (6), 491–502.

Bloom, B. S., & Masia, B. B. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals. New York: Mc Kay.

Carrillo, F. A. (2012). Can Technology Completely Replace Human Interaction in Class? World Journal on Educational Technology , 4 (3), 153-164.

Churlyaeva, N., & Kukushkin, S. (2011). Experience in applying educational technologies to the integrated system of engineering students. World Journal on Educational Technology , 3 (2), 75-89.

Colbeck, C. L., Cabrera, A. F., & Marine, R. J. (2002). Faculty Motivation To Use Alternative Teaching Methods. Arlington: National Science Foundation.

Dalgarno, B. (2001). Interpretations of constructivism and consequences for Computer Assisted Learning. British Journal of Educational Technology , 32 (2), 183–194.

Erdoğdu, B., Zaim Gökbay, İ., & Karahoca, A. (2006). Interactive Learnability Assessment For CNC Machines via Simulation And Hardware Environment in CIM Lab. Lloret de Mar: 10th International Research/Expert Conference Trends in the Development of Machinery and Associated Technology.

Farajollahi, M., & Moenikia, M. (2011). The effect of computer-based learning on distance learners’ self regulated learning strategies. World Journal on Educational Technology , 3 (1), 28-38.

Goodman, P. S., & Darr, E. D. (1998). Computer-Aided Systems and Communities: Mechanisms for Organizational Learning in Distributed Environments. Management Information Systems Quarterly , 22 (4), 417-440.

Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics , 66 (1), 64-74.

Harman, G., & Çelikler, D. (2012). Eğitimde Hazır Bulunuşluğun Önemi Üzerine Bir Derleme Çalışması. Journal of Research in Education and Teaching , 1 (3), 147-156.

Hsu, C.-W., & Lin, C.-J. (2002). A Comparison of Methods for Multiclass Support Vector Machines. IEEE Transactions On Neural Networks , 13 (2), 415-425.

Kang, M., & Im, T. (2013). Factors of learner–instructor interaction which predict perceived learning outcomes in online learning environment. Journal of Computer Assisted Learning , 29 (3), 292–301.

Karahoca, A., Karahoca, D., & Yengin, I. (2010). Computer assisted active learning system development for critical thinking in history of civilization. Cypriot Journal of Educational Sciences , 5, 04-25.

Keady, G., Fitz-Gerald, G., Gamble, G., & Sangwin, C. (2006). Computer-aided assessment in mathematical sciences. UniServe Science Assessment Symposium.

Keser, H., Uzunboylu, H., & Ozdamli, F. (2011). The trends in technology supported collaborative learning studies in 21st century. World Journal on Educational Technology , 3 (2), 103-119.

Liao, C. H., Yang, M. H., & Yang, B. C. (2013). Developing a diagnosis system of work-related capabilities for students: A computer-assisted assessment. Journal of Computer Assisted Learning , 29 (6), 530–546.

Millde-Luthander, C., Högberg, U., Nyström, M. E., Pettersson, H., Wiklund, I., & Grunewald, C. (2012). The impact of a computer assisted learning programme on the ability to interpret cardiotochography. A before and after study. Sexual & Reproductive Healthcare , 3, 37–41.

Noftle, E. E., & Robins, R. W. (2007). Personality predictors of academic outcomes: Big five correlates of GPA and SAT scores. Journal of Personality and Social Psychology , 93 (1), 116-130.

Peng, H., Long, F., & Ding, C. (2005). Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence , 27 (8), 1226-1238.

Rutten, N., van Joolingen, W. R., & van der Veen, J. T. (2012). The learning effects of computer simulations in science education. Computers & Education , 58 (1), 136–153.

Smeureanu, I., & Isaila, N. (2011). New information technologies for an innovative education. World Journal on Educational Technology , 3 (3), 177-189.

Sullivan, M. E., Ortega, A., Wasserberg, N., Kaufman, H., Nyquist, J., & Clark, R. (2008). Assessing the teaching of procedural skills: can cognitive task analysis add to our traditional teaching methods? The American Journal of Surgery , 19 (1), 20–23.

Thorndike, R. L., & Hagen, E. (1961). Measurement and evaluation in psychology and education (2nd Edition ed.). Oxford : Wiley.

Traxler, J. (2012). Mobile Learning – The Future Already Behind Us. Amman: International Conference on Interactive Mobile and Computer Aided Learning.

Uzun, L. (2012). The Internet and computer enhanced foreign language learning and intercultural communication. World Journal on Educational Technology , 4 (2), 99-112.

van Berkum, J. J., & de Jong, T. (1991). Instructional environments for simulations. Education and Computing , 6 (3-4), 305–358.

Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.

Veermans, K., van Joolingen, W. R., & de Jong, T. (2006). Using heuristics to facilitate discovery learning in a simulation learning environment in a physics domain. International Journal of Science Education , 28, 341-361.

Voogt, J., Fisser, P., Pareja Roblin, P., Tondeur, J., & van Braak, J. (2013). Technological pedagogical content knowledge – a review of the literature. Journal of Computer Assisted Learning , 29 (2), 109–121.

Voogt, J., Knezek, G., Cox, M., Knezek, D., & ten Brummelhuis, A. (2013). Under which conditions does ICT have a positive effect on teaching and learning? A Call to Action. Journal of Computer Assisted Learning , 29 (1), 4–14.

Zaim Gökbay, İ., & Erdoğdu, B. (2006). Computer Aided Learning Assessment of Simulation and Hardware Environment for CNC Lathe Machine in CIM Laboratory. Iasi: 4th European Conference on Intelligent Systems and Technologies.

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