Research Article
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Year 2025, Volume: 16 Issue: 1, 1 - 12, 31.03.2025
https://doi.org/10.21031/epod.1525454

Abstract

References

  • Agresti, A. (2013). Categorical data analysis. Wiley.
  • Al-Saleem, M., Al-Kathiry, N., Al-Osimi, S., & Badr, G. (2015). Mining educational data to predict students’ academic performance. In Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings 11 (pp. 403-414). Springer International Publishing.
  • Ashwin, T. S., & Guddeti, R. M. R. (2020). Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Education and Information Technologies, 25(2), 1387-1415. https://doi.org/10.1007/s10639-019-10004-6
  • Aybek, H. S. Y., & Okur, M. R. (2018). Predicting achievement with artificial neural networks: The case of Anadolu University open education system. International Journal of Assessment Tools in Education, 5(3), 474-490. https://doi.org/10.21449/ijate.435507
  • Aydoğan, İ., & Zırhlıoğlu, G. (2018). Öğrenci başarılarının yapay sinir ağları ile kestirilmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 15(1), 577-610. http://dx.doi.org/10.23891/efdyyu.2018.80
  • Aydoğdu, Ş. (2020). Predicting student final performance using artificial neural networks in online learning environments. Education and Information Technologies, 25(3), 1913-1927. https://doi.org/10.1007/s10639-019-10053-x
  • Bakkialakshmi, V. S., Sudalaimuthu, T., & Winkler, S. (2022). Effective Prediction System for Affective Computing on Emotional Psychology with Artificial Neural Network. Easy Chair Preprint.
  • Beck, M.W. (2018). NeuralNetTools: Visualization and Analysis Tools for Neural Networks. Journal of Statistical Software, 85(11), 1 .https://doi.org/10.18637/jss.v085.i11
  • Beck, M.W. (2022). Visualization and analysis tools for neural networks, R package version 1.5.3. Retrieved from https://cran.r-project.org/web/packages/NeuralNetTools/index.html
  • Brownlee, J. (2020). Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery.
  • Carstensen, S. L., Madsen, J., & Larsen, J. (2016). Predicting Changes in Affective States using Neural Networks. arXiv preprint arXiv:1612.00582. https://doi.org/10.48550/arXiv.1612.00582
  • Chan, K. Y., Kwong, C. K., Wongthongtham, P., Jiang, H., Fung, C. K., Abu-Salih, B., ... & Jain, P. (2020). Affective design using machine learning: a survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 33(7), 645-669. https://doi.org/10.1080/0951192X.2018.1526412
  • Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J. M., & Raymundo, C. (2023, February). Artificial neural network model to predict student performance using nonpersonal information. In Frontiers in Education (Vol. 8, p. 1106679). Frontiers Media SA.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  • Ennett, C. M., Frize, M., & Walker, C. R. (2001). Influence of missing values on artificial neural network performance. In MEDINFO 2001 (pp. 449-453). Ios Press.
  • Feng, H. (2022). A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features. Computational Intelligence and Neuroscience, 2022(1), 2114114. https://doi.org/10.1155/2022/2114114
  • Flitman, A. M. (1997). Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research, 24(4), 367-377. https://doi.org/10.1016/S0305-0548(96)00060-3
  • Fritsch, S., Guenther, F., Wright, M.N., Suling, M., Mueller, S.M. (2019). Training of neural Networks, R package version 1.44.2. Retrieved from https://cran.r-project.org/web/packages/neuralnet/index.html
  • George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.
  • Goel, A., Goel, A. K., & Kumar, A. (2023). The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research, 31(3), 275-285. https://doi.org/10.1007/s41324-022-00494-x
  • Grosan, C., & Abraham, A. (2011). Artificial neural networks. Intelligent Systems: A Modern Approach, 281-323.
  • Guarín, C. E. L., Guzmán, E. L., & González, F. A. (2015). A model to predict low academic performance at a specific enrollment using data mining. IEEE Revista Iberoamericana de tecnologias del Aprendizaje, 10(3), 119-125. https://doi.org/10.1109/RITA.2015.2452632
  • Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), 416-434. https://doi.org/10.1016/j.neuron.2019.12.002
  • Hox, J., Moerbeek, M., & van de Schoot, R. (2018). Multilevel analysis: Techniques and applications (3rd ed.). Routledge.
  • Huang, S., & Fang, N. (2012, October). Work in progress: Early prediction of students' academic performance in an introductory engineering course through different mathematical modeling techniques. In 2012 Frontiers in Education Conference Proceedings (pp. 1-2). IEEE.
  • Jadhav, N., & Sugandhi, R. (2018, November). Survey on human behavior recognition using affective computing. In 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN) (pp. 98-103). IEEE.
  • Jamisola, R. S. (2016). Conceptualizing a Questionnaire-Based Machine Learning Tool that Determines State of Mind and Emotion. Lovotics, 4(115), 2. http://dx.doi.org/10.4172/2090-9888.1000115
  • Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1-11. https://doi.org/10.1016/j.compedu.2013.01.015
  • Kose, U., & Arslan, A. (2017). Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm. Computer Applications in Engineering Education, 25(1), 142-156. https://doi.org/10.1002/cae.21787
  • Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., … & Hunt, T. (2023). Classification and regression training, R package version 6.0-94. Retrieved from https://cran.r-project.org/web/packages/caret/index.html
  • Landis, J, R., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.https://doi.org/10.2307/2529310
  • Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 982. https://doi.org/10.1007/s42452-019-0884-7
  • Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., & Aulagnier, S. (1996). Application of neural networks to modeling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52. https://doi.org/10.1016/0304-3800(95)00142-5
  • Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6), 335-346. https://doi.org/10.1038/s41583-020-0277-3
  • Liu, J., Ang, M. C., Chaw, J. K., Kor, A. L., & Ng, K. W. (2023). Emotion assessment and application in human–computer interaction interface based on backpropagation neural network and artificial bee colony algorithm. Expert Systems with Applications, 232, 120857. https://doi.org/10.1016/j.eswa.2023.120857
  • McCarthy, N. (June19, 2019). Lebanon has by far the most refugees per 1,000 population. Retrieved from https://www.statista.com/chart/8800/lebanon-has-by-far-the-most-refugees-per-capita/
  • OECD. (2018). PISA 2018 Technical report. OECD Publishing, Retrieved from https://www.oecd.org/pisa/data/pisa2018technicalreport/
  • Orozco-del-Castillo, M. G., Orozco-del-Castillo, E. C., Brito-Borges, E., Bermejo-Sabbagh, C., & Cuevas-Cuevas, N. (2021, November). An artificial neural network for depression screening and questionnaire refinement in undergraduate students. In International Congress of Telematics and Computing (pp. 1-13). Springer International Publishing.
  • Öztemel, E. (2003).Yapay sinir ağları. PapatyaYayıncılık.
  • Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., ... & Yoon, H. J. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean Medical Science, 35(42). https://doi.org/10.3346/jkms.2020.35.e379
  • Rashid, T. A., & Ahmad, H. A. (2016). Lecturer performance system using neural network with Particle Swarm Optimization. Computer Applications in Engineering Education, 24(4), 629-638. https://doi.org/10.1002/cae.21737
  • Ripley, B., & Venables, W. (2023). Feed-forward neural networks and multinomial log-linear models, R package version 7.3-18. Retrieved from https://cran.r-project.org/web/packages/nnet/index.html
  • Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/10.1016/j.caeai.2021.100018
  • Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L., Savalei, V., Merkle, E., … & Jorgensen, T. (2024). Latent variable analysis, R package version 0.6-18. Retrieved from https://cran.r-project.org/web/packages/lavaan/lavaan.pdf
  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  • Şen, S. (2020). Mplus ile yapısal eşitlik modellemesi uygulamaları. Nobel Akademik Yayıncılık.
  • Tabachnick, B. G., & Fidell, L. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11), 1225-1231. https://doi.org/10.1016/S0895-4356(96)00002-9
  • Umar, M. A. (2019). Student academic performance prediction using artificial neural networks: A case study. International Journal of Computer Applications, 178(48), 24-29. http://dx.doi.org/10.5120/ijca2019919387
  • Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405. https://doi.org/10.1080/09645290701409939
  • Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., ... & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83, 19-52. https://doi.org/10.1016/j.inffus.2022.03.009
  • Zacharis, N. Z. (2016). Predicting student academic performance in blended learning using artificial neural networks. International Journal of Artificial Intelligence and Applications, 7(5), 17-29. http://dx.doi.org/10.5121/ijaia.2016.7502
  • Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-60327-101-1_2

Investigating the Performance of Artificial Neural Networks in Predicting Affective Responses

Year 2025, Volume: 16 Issue: 1, 1 - 12, 31.03.2025
https://doi.org/10.21031/epod.1525454

Abstract

In this study it is aimed to examine the performance of an artificial neural network trained using items reflecting a latent trait in predicting responses to an item reflecting the same trait. This latent trait is the awareness of being able to communicate with people from different cultures, which is included in the PISA 2018 application. Relevant scale items were used as research variables. In addition to determining the extent to which the predicted responses overlap with the actual responses by analyzing the artificial neural network models, it was examined how the predicted responses affect the assumed latent construct and the reliability of the responses. Thus, the performance of artificial neural networks in predicting responses to affective items was evaluated.

References

  • Agresti, A. (2013). Categorical data analysis. Wiley.
  • Al-Saleem, M., Al-Kathiry, N., Al-Osimi, S., & Badr, G. (2015). Mining educational data to predict students’ academic performance. In Machine Learning and Data Mining in Pattern Recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings 11 (pp. 403-414). Springer International Publishing.
  • Ashwin, T. S., & Guddeti, R. M. R. (2020). Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Education and Information Technologies, 25(2), 1387-1415. https://doi.org/10.1007/s10639-019-10004-6
  • Aybek, H. S. Y., & Okur, M. R. (2018). Predicting achievement with artificial neural networks: The case of Anadolu University open education system. International Journal of Assessment Tools in Education, 5(3), 474-490. https://doi.org/10.21449/ijate.435507
  • Aydoğan, İ., & Zırhlıoğlu, G. (2018). Öğrenci başarılarının yapay sinir ağları ile kestirilmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 15(1), 577-610. http://dx.doi.org/10.23891/efdyyu.2018.80
  • Aydoğdu, Ş. (2020). Predicting student final performance using artificial neural networks in online learning environments. Education and Information Technologies, 25(3), 1913-1927. https://doi.org/10.1007/s10639-019-10053-x
  • Bakkialakshmi, V. S., Sudalaimuthu, T., & Winkler, S. (2022). Effective Prediction System for Affective Computing on Emotional Psychology with Artificial Neural Network. Easy Chair Preprint.
  • Beck, M.W. (2018). NeuralNetTools: Visualization and Analysis Tools for Neural Networks. Journal of Statistical Software, 85(11), 1 .https://doi.org/10.18637/jss.v085.i11
  • Beck, M.W. (2022). Visualization and analysis tools for neural networks, R package version 1.5.3. Retrieved from https://cran.r-project.org/web/packages/NeuralNetTools/index.html
  • Brownlee, J. (2020). Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery.
  • Carstensen, S. L., Madsen, J., & Larsen, J. (2016). Predicting Changes in Affective States using Neural Networks. arXiv preprint arXiv:1612.00582. https://doi.org/10.48550/arXiv.1612.00582
  • Chan, K. Y., Kwong, C. K., Wongthongtham, P., Jiang, H., Fung, C. K., Abu-Salih, B., ... & Jain, P. (2020). Affective design using machine learning: a survey and its prospect of conjoining big data. International Journal of Computer Integrated Manufacturing, 33(7), 645-669. https://doi.org/10.1080/0951192X.2018.1526412
  • Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J. M., & Raymundo, C. (2023, February). Artificial neural network model to predict student performance using nonpersonal information. In Frontiers in Education (Vol. 8, p. 1106679). Frontiers Media SA.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.1177/001316446002000104
  • Ennett, C. M., Frize, M., & Walker, C. R. (2001). Influence of missing values on artificial neural network performance. In MEDINFO 2001 (pp. 449-453). Ios Press.
  • Feng, H. (2022). A Novel Adaptive Affective Cognition Analysis Model for College Students Using a Deep Convolution Neural Network and Deep Features. Computational Intelligence and Neuroscience, 2022(1), 2114114. https://doi.org/10.1155/2022/2114114
  • Flitman, A. M. (1997). Towards analysing student failures: neural networks compared with regression analysis and multiple discriminant analysis. Computers & Operations Research, 24(4), 367-377. https://doi.org/10.1016/S0305-0548(96)00060-3
  • Fritsch, S., Guenther, F., Wright, M.N., Suling, M., Mueller, S.M. (2019). Training of neural Networks, R package version 1.44.2. Retrieved from https://cran.r-project.org/web/packages/neuralnet/index.html
  • George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4th ed.). Allyn & Bacon.
  • Goel, A., Goel, A. K., & Kumar, A. (2023). The role of artificial neural network and machine learning in utilizing spatial information. Spatial Information Research, 31(3), 275-285. https://doi.org/10.1007/s41324-022-00494-x
  • Grosan, C., & Abraham, A. (2011). Artificial neural networks. Intelligent Systems: A Modern Approach, 281-323.
  • Guarín, C. E. L., Guzmán, E. L., & González, F. A. (2015). A model to predict low academic performance at a specific enrollment using data mining. IEEE Revista Iberoamericana de tecnologias del Aprendizaje, 10(3), 119-125. https://doi.org/10.1109/RITA.2015.2452632
  • Hasson, U., Nastase, S. A., & Goldstein, A. (2020). Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron, 105(3), 416-434. https://doi.org/10.1016/j.neuron.2019.12.002
  • Hox, J., Moerbeek, M., & van de Schoot, R. (2018). Multilevel analysis: Techniques and applications (3rd ed.). Routledge.
  • Huang, S., & Fang, N. (2012, October). Work in progress: Early prediction of students' academic performance in an introductory engineering course through different mathematical modeling techniques. In 2012 Frontiers in Education Conference Proceedings (pp. 1-2). IEEE.
  • Jadhav, N., & Sugandhi, R. (2018, November). Survey on human behavior recognition using affective computing. In 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN) (pp. 98-103). IEEE.
  • Jamisola, R. S. (2016). Conceptualizing a Questionnaire-Based Machine Learning Tool that Determines State of Mind and Emotion. Lovotics, 4(115), 2. http://dx.doi.org/10.4172/2090-9888.1000115
  • Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1-11. https://doi.org/10.1016/j.compedu.2013.01.015
  • Kose, U., & Arslan, A. (2017). Optimization of self‐learning in Computer Engineering courses: An intelligent software system supported by Artificial Neural Network and Vortex Optimization Algorithm. Computer Applications in Engineering Education, 25(1), 142-156. https://doi.org/10.1002/cae.21787
  • Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., … & Hunt, T. (2023). Classification and regression training, R package version 6.0-94. Retrieved from https://cran.r-project.org/web/packages/caret/index.html
  • Landis, J, R., & Koch, G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.https://doi.org/10.2307/2529310
  • Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 982. https://doi.org/10.1007/s42452-019-0884-7
  • Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., & Aulagnier, S. (1996). Application of neural networks to modeling nonlinear relationships in ecology. Ecological Modelling, 90, 39–52. https://doi.org/10.1016/0304-3800(95)00142-5
  • Lillicrap, T. P., Santoro, A., Marris, L., Akerman, C. J., & Hinton, G. (2020). Backpropagation and the brain. Nature Reviews Neuroscience, 21(6), 335-346. https://doi.org/10.1038/s41583-020-0277-3
  • Liu, J., Ang, M. C., Chaw, J. K., Kor, A. L., & Ng, K. W. (2023). Emotion assessment and application in human–computer interaction interface based on backpropagation neural network and artificial bee colony algorithm. Expert Systems with Applications, 232, 120857. https://doi.org/10.1016/j.eswa.2023.120857
  • McCarthy, N. (June19, 2019). Lebanon has by far the most refugees per 1,000 population. Retrieved from https://www.statista.com/chart/8800/lebanon-has-by-far-the-most-refugees-per-capita/
  • OECD. (2018). PISA 2018 Technical report. OECD Publishing, Retrieved from https://www.oecd.org/pisa/data/pisa2018technicalreport/
  • Orozco-del-Castillo, M. G., Orozco-del-Castillo, E. C., Brito-Borges, E., Bermejo-Sabbagh, C., & Cuevas-Cuevas, N. (2021, November). An artificial neural network for depression screening and questionnaire refinement in undergraduate students. In International Congress of Telematics and Computing (pp. 1-13). Springer International Publishing.
  • Öztemel, E. (2003).Yapay sinir ağları. PapatyaYayıncılık.
  • Park, C. W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., ... & Yoon, H. J. (2020). Artificial intelligence in health care: Current applications and issues. Journal of Korean Medical Science, 35(42). https://doi.org/10.3346/jkms.2020.35.e379
  • Rashid, T. A., & Ahmad, H. A. (2016). Lecturer performance system using neural network with Particle Swarm Optimization. Computer Applications in Engineering Education, 24(4), 629-638. https://doi.org/10.1002/cae.21737
  • Ripley, B., & Venables, W. (2023). Feed-forward neural networks and multinomial log-linear models, R package version 7.3-18. Retrieved from https://cran.r-project.org/web/packages/nnet/index.html
  • Rodríguez-Hernández, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/10.1016/j.caeai.2021.100018
  • Rosseel, Y., Oberski, D., Byrnes, J., Vanbrabant, L., Savalei, V., Merkle, E., … & Jorgensen, T. (2024). Latent variable analysis, R package version 0.6-18. Retrieved from https://cran.r-project.org/web/packages/lavaan/lavaan.pdf
  • Shahiri, A. M., & Husain, W. (2015). A review on predicting student's performance using data mining techniques. Procedia Computer Science, 72, 414-422. https://doi.org/10.1016/j.procs.2015.12.157
  • Şen, S. (2020). Mplus ile yapısal eşitlik modellemesi uygulamaları. Nobel Akademik Yayıncılık.
  • Tabachnick, B. G., & Fidell, L. (2019). Using multivariate statistics (7th ed.). Pearson.
  • Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11), 1225-1231. https://doi.org/10.1016/S0895-4356(96)00002-9
  • Umar, M. A. (2019). Student academic performance prediction using artificial neural networks: A case study. International Journal of Computer Applications, 178(48), 24-29. http://dx.doi.org/10.5120/ijca2019919387
  • Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting academic performance by data mining methods. Education Economics, 15(4), 405. https://doi.org/10.1080/09645290701409939
  • Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., ... & Zhang, W. (2022). A systematic review on affective computing: Emotion models, databases, and recent advances. Information Fusion, 83, 19-52. https://doi.org/10.1016/j.inffus.2022.03.009
  • Zacharis, N. Z. (2016). Predicting student academic performance in blended learning using artificial neural networks. International Journal of Artificial Intelligence and Applications, 7(5), 17-29. http://dx.doi.org/10.5121/ijaia.2016.7502
  • Zou, J., Han, Y., & So, S. S. (2009). Overview of artificial neural networks. Artificial neural networks: methods and applications, 14-22. https://doi.org/10.1007/978-1-60327-101-1_2
There are 53 citations in total.

Details

Primary Language English
Subjects Testing, Assessment and Psychometrics (Other)
Journal Section Articles
Authors

Izzettin Aydogan 0000-0002-5908-1285

Osman Tat 0000-0003-2950-9647

Publication Date March 31, 2025
Submission Date July 31, 2024
Acceptance Date November 19, 2024
Published in Issue Year 2025 Volume: 16 Issue: 1

Cite

APA Aydogan, I., & Tat, O. (2025). Investigating the Performance of Artificial Neural Networks in Predicting Affective Responses. Journal of Measurement and Evaluation in Education and Psychology, 16(1), 1-12. https://doi.org/10.21031/epod.1525454