Developing a game-based test to assess middle school sixth-grade students’ algorithmic thinking skills
Yıl 2024,
, 88 - 108, 16.03.2024
Emre Zengin
,
Yasemin Karal
Öz
This study was carried out to develop a test to assess algorithmic thinking skills. To this end, the twelve steps suggested by Downing (2006) were adopted. Throughout the test development, 24 middle school sixth-grade students and eight experts in different areas took part as needed in the tasks on the project. The test was given to 252 students attending the sixth grade who were selected through purposeful sampling. The content validity of the test was ensured by means of obtaining expert opinion, whereas the construct validity was ensured by performing an independent sample t-test on the difference between the lower and upper groups. As a result, the algorithmic thinking skills assessment test was finalized with 22 main items and 2 sample items, totalling 24 items. The KR-20 reliability analysis proved a quite reliable test based on the reliability coefficient of 0.83. As mentioned earlier, the independent sample t-test was applied to the difference of lower and upper groups for construct validation of the test. It was seen that the test items are significant in discriminating the students in the lower and upper groups (p<0.01).
Etik Beyan
Trabzon University, 13.08.2021, E-81614018-000-704.
Kaynakça
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- Apostolellis, P., Stewart, M., Frisina, C., & Kafura, D. (2014). RaBit EscAPE: A board game for computational thinking. In Proceedings of the 2014 conference on Interaction design and children (pp. 349-352). http://dx.doi.org/10.1145/2593968.2610489
- Ayala, N.A.R., Mendívil, E.G., Salinas, P., & Rios, H. (2013). Kinesthetic learning applied to mathematics using kinect. Procedia Computer Science, 25, 131 135. https://doi.org/10.1016/j.procs.2013.11.016
- Aytekin, A., Çakır, F.S., Yücel, Y.B., & Kulaözü, İ. (2018). The place and importance of algorithms in our lives. Eurasian Journal of Social and Economic Studies, 5(7), 143 150. https://dergipark.org.tr/tr/pub/asead/issue/41013/495619
- Başol, G. (2019). Measurement and evaluation in education. Pegem Citation Index, 001-307.
- Baykul, Y., Gelbal, S., & Kelecioğlu, H. (2003). Measurement and evaluation in education for Anatolian teacher high schools. National Education Printing House.
- Bayrakçeken, S. (2015). Test development. E. Karip (Ed.), In Measurement and Evaluation (s. 292-322). Pegem Academy.
- Bellocchi, A., King, D.T., & Ritchie, S.M. (2016). Context-based assessment: Creating opportunities for resonance between classroom fields and societal fields. International Journal of Science Education, 38(8), 1304 1342. https://doi.org/10.1080/09500693.2016.1189107
- Borkulo, S., Chytas, C., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Computational thinking in the mathematics classroom: Fostering algorithmic thinking and generalization skills using dynamic mathematics software. In The 16th Workshop in Primary and Secondary Computing Education (pp. 1 9). https://doi.org/10.1145/3481312.3481319.
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- Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö.E., Karadeniz, Ş., & Demirel, F. (2020). Scientific research methods. Pegem Publications.
- Chen, K.Z., & Chi, H.H. (2022). Novice young board-game players’ experience about computational thinking. Interactive Learning Environments, 30(8), 1375-1387. https://doi.org/10.1080/10494820.2020.1722712
- Chuechote, S., Nokkaew, A., Phongsasithorn, A., & Laosinchai, P. (2020). A neo-piagetian analysis of algorithmic thinking development through the “sorted” digital game. Contemporary Educational Technology, 12(1), 1 15. http://dx.doi.org/10.30935/cet.685959
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- Czakóová, K., & Udvaros, J. (2021). Applications and games for the development of algorithmic thinking in favor of experiential learning. In EDULEARN21 Proceedings (pp. 6873-6879). IATED. https://doi.org/10.21125/edulearn.2021.1389
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- Erümit, K.A., Karal, H., Şahin, G., Aksoy, D.A., Aksoy, A., & Benzer, A.İ. (2018). A model proposal for teaching programming: programming in seven steps. Education and Science, 44(197), 1-29. http://dx.doi.org/10.15390/EB.2018.7678
- Evripidou, S., Amanatiadis, A., Christodoulou, K., & Chatzichristofis, S.A. (2021). Introducing algorithmic thinking and sequencing using tangible robots. IEEE Transactions on Learning Technologies, 14(1), 93 105. https://doi.org/10.1109/TLT.2021.3058060
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Developing a game-based test to assess middle school sixth-grade students’ algorithmic thinking skills
Yıl 2024,
, 88 - 108, 16.03.2024
Emre Zengin
,
Yasemin Karal
Öz
This study was carried out to develop a test to assess algorithmic thinking skills. To this end, the twelve steps suggested by Downing (2006) were adopted. Throughout the test development, 24 middle school sixth-grade students and eight experts in different areas took part as needed in the tasks on the project. The test was given to 252 students attending the sixth grade who were selected through purposeful sampling. The content validity of the test was ensured by means of obtaining expert opinion, whereas the construct validity was ensured by performing an independent sample t-test on the difference between the lower and upper groups. As a result, the algorithmic thinking skills assessment test was finalized with 22 main items and 2 sample items, totalling 24 items. The KR-20 reliability analysis proved a quite reliable test based on the reliability coefficient of 0.83. As mentioned earlier, the independent sample t-test was applied to the difference of lower and upper groups for construct validation of the test. It was seen that the test items are significant in discriminating the students in the lower and upper groups (p<0.01).
Etik Beyan
Trabzon University, 13.08.2021, E-81614018-000-704
Kaynakça
- Altakrouri, B., & Schrader, A. (2012, September). Towards dynamic natural interaction ensembles. In the 26th BCS Conference on Human Computer Interaction 26 (pp. 1-10). http://dx.doi.org/10.14236/ewic/HCI2012.0
- Anderson, M.L. (2003). Embodied cognition: A field guide. Artificial intelligence, 149(1), 91-130. https://doi.org/10.1016/S0004-3702(03)00054-7
- Apostolellis, P., Stewart, M., Frisina, C., & Kafura, D. (2014). RaBit EscAPE: A board game for computational thinking. In Proceedings of the 2014 conference on Interaction design and children (pp. 349-352). http://dx.doi.org/10.1145/2593968.2610489
- Ayala, N.A.R., Mendívil, E.G., Salinas, P., & Rios, H. (2013). Kinesthetic learning applied to mathematics using kinect. Procedia Computer Science, 25, 131 135. https://doi.org/10.1016/j.procs.2013.11.016
- Aytekin, A., Çakır, F.S., Yücel, Y.B., & Kulaözü, İ. (2018). The place and importance of algorithms in our lives. Eurasian Journal of Social and Economic Studies, 5(7), 143 150. https://dergipark.org.tr/tr/pub/asead/issue/41013/495619
- Başol, G. (2019). Measurement and evaluation in education. Pegem Citation Index, 001-307.
- Baykul, Y., Gelbal, S., & Kelecioğlu, H. (2003). Measurement and evaluation in education for Anatolian teacher high schools. National Education Printing House.
- Bayrakçeken, S. (2015). Test development. E. Karip (Ed.), In Measurement and Evaluation (s. 292-322). Pegem Academy.
- Bellocchi, A., King, D.T., & Ritchie, S.M. (2016). Context-based assessment: Creating opportunities for resonance between classroom fields and societal fields. International Journal of Science Education, 38(8), 1304 1342. https://doi.org/10.1080/09500693.2016.1189107
- Borkulo, S., Chytas, C., Drijvers, P., Barendsen, E., & Tolboom, J. (2021). Computational thinking in the mathematics classroom: Fostering algorithmic thinking and generalization skills using dynamic mathematics software. In The 16th Workshop in Primary and Secondary Computing Education (pp. 1 9). https://doi.org/10.1145/3481312.3481319.
- Brown, W. (2015). Introduction to algorithmic thinking. https://raptor.martincarlisle.com/Introduction%20to%20Algorithmic%20Thinking.doc
- Büyüköztürk, Ş., Kılıç-Çakmak, E., Akgün, Ö.E., Karadeniz, Ş., & Demirel, F. (2020). Scientific research methods. Pegem Publications.
- Chen, K.Z., & Chi, H.H. (2022). Novice young board-game players’ experience about computational thinking. Interactive Learning Environments, 30(8), 1375-1387. https://doi.org/10.1080/10494820.2020.1722712
- Chuechote, S., Nokkaew, A., Phongsasithorn, A., & Laosinchai, P. (2020). A neo-piagetian analysis of algorithmic thinking development through the “sorted” digital game. Contemporary Educational Technology, 12(1), 1 15. http://dx.doi.org/10.30935/cet.685959
- Czakóová, K. (2020). Developing algorithmic thinking by educational computer games. Paper presented at the Conference eLearning and Software for Educationtional, Romania. http://dx.doi.org/10.12753/2066-026X-20-003
- Czakóová, K., & Udvaros, J. (2021). Applications and games for the development of algorithmic thinking in favor of experiential learning. In EDULEARN21 Proceedings (pp. 6873-6879). IATED. https://doi.org/10.21125/edulearn.2021.1389
- Çetin, B. (Ed.) (2019). Measurement and evaluation in education. Anı Publishing.
- Debabi, W., & Bensebaa, T. (2016). Using serious game to enhance learning and teaching algorithmic. Journal of e Learning and Knowledge Society, 12(2). http://dx.doi.org/10.20368/1971-8829/1125
- Demir, O., & Köse, İ.A. (2014). Comparison of cutoff scores determined by Angoff, Nedelsky and Ebel standard setting methods. Journal of Mersin University Faculty of Education, 10(2), 14 27. https://dergipark.org.tr/en/pub/mersinefd/issue/17394/181823?publisher=mersin
- Doleck, T., Bazelais, P., Lemay, D.J., Saxena, A., & Basnet, R.B. (2017). Algorithmic thinking, cooperativity, creativity, critical thinking, and problem solving: Exploring the relationship between computational thinking skills and academic performance. Journal of Computers in Education, 4(4), 355-369. https://doi.org/10.1007/s40692-017-0090-9
- Downing, S.M. (2006). Twelve steps for effective test development. Handbook of test development, 3, 25. https://doi.org/10.4324/9780203874776.ch1
- Elshahawy, M., Aboelnaga, K., & Sharaf, N. (2020). Codaroutine: A serious game for introducing sequential programming concepts to children with autism. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1862 1867). IEEE. http://dx.doi.org/10.1109/EDUCON45650.2020.9125196
- Erümit, K.A., Karal, H., Şahin, G., Aksoy, D.A., Aksoy, A., & Benzer, A.İ. (2018). A model proposal for teaching programming: programming in seven steps. Education and Science, 44(197), 1-29. http://dx.doi.org/10.15390/EB.2018.7678
- Evripidou, S., Amanatiadis, A., Christodoulou, K., & Chatzichristofis, S.A. (2021). Introducing algorithmic thinking and sequencing using tangible robots. IEEE Transactions on Learning Technologies, 14(1), 93 105. https://doi.org/10.1109/TLT.2021.3058060
- Fensham, P.J., & Rennie, L.J. (2013). Towards an authentically assessed science curriculum. In Valuing assessment in science education: Pedagogy, curriculum, policy (pp. 69-100). Springer.
- Figueiredo, M., Amante, S., Gomes, H.M.D.S.V., Gomes, M.A., Rego, B., Alves, V., & Duarte, R.P. (2021). Algorithmic thinking in early childhood education: Opportunities and supports in the Portuguese context. In EDULEARN21 Proceedings (pp. 9339-9348). IATED. https://doi.org/10.22521/edupij.2022.112.3
- Futschek, G., & Moschitz, J. (2010). Developing algorithmic thinking by inventing and playing algorithms. Proceedings of the 2010 constructionist approaches to creative learning, thinking and education: Lessons for the 21st century (constructionism 2010), 1-10.
- Gall, M.D., Barg, W.R., & Gall, j.P. (1996). Educational research: an introduction (6th ed.). Longman Publishing.
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