Italian Journal of Health Education, Sports and Inclusive Didactics
LEARNING MOTIVATION: PREDICTIVE MODEL WITH MACHINE LEARNING ANALYSIS
Abstract
The direction of development undertaken by the world of research in the field of education has gradually been oriented to the identification of tools capable of collecting and implementing the considerable amount of data that can be extrapolated in this context, in order to identify improvement trajectories common to the various application scenarios. Therefore, the operationalization of the numerous theoretical paradigms linked to education plays a prominent role. In this sense, learning motivation is one of the most studied constructs in the field of educational neuroscience, due to the implications associated with it in mediating learning processes. Within this context, the project presented aims to operationalize the learning motivation through the inclusion of specific factor variables (socio-demographic, cognitive, affective and intra-individual) in a machine learning algorithm, structured on the basis of the evidence in the literature on the characteristics that allow to identify the construct at an inter-subjective level. This algorithm is part of the wide range of studies related to Educational Data Mining, with the aim of structuring a predictive model that allows to outline the various profiles of learning motivation of individual students, with the aim of creating a useful tool to identify appropriate learning paths customized on the basis of the motivational characteristics of each student.
Keywords
Full Text:
PDF (Italiano)References
Albreiki B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’performance prediction using machine learning techniques. Education Sciences, 11(9), 552.
Aleem A., & Gore, M.M. (2020). Educational data mining methods: A survey. In 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT) (pp. 182-188). IEEE.
Aubé B., Rougier M., Muller D., Ric F., Yzerbyt V. (2019). The online-VAAST: A short and online tool to measure spontaneous approach and avoidance tendencies. Acta Psychol (Amst). 201:102942. doi: 10.1016/j.actpsy.2019.102942
Bakar R. (2014). The effect of learning motivation on student’s productive competencies in vocational high school, west Sumatra. International Journal of Asian Social Science. 4(6):722-732.
Baradwaj B. K., & Pal S. (2012). Mining educational data to analyze students' performance. arXiv preprint arXiv:1201.3417.
Bråten I., Olaussen B.S. (2005). Profiling individual differences in student motivation: A longitudinal cluster-analytic study in different academic contexts. Contemporary Educational Psychology. Volume 30, Issue 3, Pages 359-396, ISSN 0361 476X, https://doi.org/10.1016/j.cedpsych.2005.01.003.
Bryant K. G., & Barker J. M. (2020). Arbitration of Approach-Avoidance Conflict by Ventral Hippocampus. Frontiers in neuroscience, 14, 615337. https://doi.org/10.3389/fnins.2020.615337
Carbonell J. G., Michalski R.S., & Mitchell T.M. (1983). An overview of machine learning. Machine learning, 3-23.
Chakraborty S., & Nagwani N.K. (2014). Analysis and study of Incremental DBSCAN clustering algorithm. arXiv preprint arXiv:1406.4754.
Cunningham W.A. & Brosch T. (2012). Motivational Salience: Amygdala Tuning From Traits, Needs, Values, and Goals. Current Directions in Psychological Science. 2012;21(1):54-59. doi:10.1177/0963721411430832
Dutt M., Ismail A., & Herawan T. (2017). A Systematic Review on Educational Data Mining in IEEE Access, vol. 5, pp. 15991-16005, doi: 10.1109/ACCESS.2017.2654247.
El Naqa I., & Murphy M.J. (2015). What is machine learning? In machine learning in radiation oncology (pp. 3-11). Springer, Cham.
Elliot A.J., & Church, M.A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218–232. https://doi.org/10.1037/0022-3514.72.1.218
Elliot A.J. (2006). The Hierarchical Model of Approach-Avoidance Motivation. Motiv Emot 30, 111–116 https://doi.org/10.1007/s11031-006-9028-7
Ester M., Kriegel H.P., Sander J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
Filgona J., Sakiyo J., Gwany D. & Okoronka A. (2020). Motivation in Learning. Asian Journal of Education and Social Studies. 10. 16-37. 10.9734/AJESS/2020/v10i430273.
Froiland, J. M., & Worrell, F. C. (2016). Intrinsic motivation, learning goals, engagement, and achievement in a diverse high school. Psychology in the Schools, 53(3), 321–336. https://doi.org/10.1002/pits.21901
Gajewski P.D., Hanisch E., Falkenstein M., Thönes S., & Wascher E. (2018). What Does the n-Back Task Measure as We Get Older? Relations Between Working-Memory Measures and Other Cognitive Functions Across the Lifespan. Frontiers in psychology, 9, 2208. https://doi.org/10.3389/fpsyg.2018.02208
Gallistel CR & Matzel LD (2013). The neuroscience of learning: beyond the Hebbian synapse. Annu Rev Psychol. 2013;64:169-200. doi: 10.1146/annurev-psych-113011-143807.
Gibbens B. (2019). Measuring student motivation in an introductory biology class. The American Biology Teacher. 81(1):20– 26
Jamesmanoharan, J., Ganesh, S. H., Felciah, M. L. P., & Shafreenbanu, A. K. (2014). Discovering Students' Academic Performance Based on GPA Using K-Means Clustering Algorithm. In 2014 World Congress on Computing and Communication Technologies (pp. 200-202). IEEE.
Jiang T., Gradus J.L., & Rosellini A.J. (2020). Supervised machine learning: a brief primer. Behavior Therapy, 51(5), 675-687.
Khan K., Rehman S. U., Aziz K., Fong S., & Sarasvady S. (2014). DBSCAN: Past, present and future. In The fifth international conference on the applications of digital information and web technologies (ICADIWT 2014) (pp. 232-238). IEEE.
Kotsiantis S. B., Zaharakis I., & Pintelas P. (2007). Supervised machine learning: A review of classification techniques. Emerging artificial intelligence applications in computer engineering, 160(1), 3-24.
Kryszkiewicz M., & Lasek P. (2010). TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality. In International Conference on Rough Sets and Current Trends in Computing (pp. 60-69). Springer, Berlin, Heidelberg.
Kumar K.M., & Reddy A.R.M. (2016). A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recognition, 58, 39-48.
Kuncel N.R., Hezlett S.A., & Ones D.S. (2004). Academic performance, career potential, creativity, and job performance: Can one construct predict them all?. Journal of personality and social psychology, 86(1), 148.
Lamers M.J.M., Roelofs A., Rabeling-Keus I.M. (2010). Selective attention and response set in the Stroop task. Memory & Cognition 38, 893–904. https://doi.org/10.3758/MC.38.7.893
Linnenbrink E.A., & Pintrich P.R. (2003). The role of self-efficacy beliefs instudent engagement and learning intheclassroom. Reading &Writing Quarterly, 19(2), 119-137.
MacLeod C.M. (1991). Half a century of research on the Stroop: An integrative review. Psychological Bulletin, 109, 163-203.
Meleddu M., & Scalas L.F. (2009). Rendimento scolastico, fattori della personalità, processi motivazionali e sistema del se: una rassegna sullo sviluppo degli studi. Giornale di psicologia, 3, 297-318.
Mendes W.B., & Park J. (2014). Neurobiological concomitants of motivational states. In A. J. Elliot, Advances in motivation science, Vol. 1, pp. 233–270). Elsevier Academic Press.
Meule A. (2017). Reporting and Interpreting Working Memory Performance in n-back Tasks. Frontiers in psychology, 8, 352. https://doi.org/10.3389/fpsyg.2017.00352
Normah C.D., Edbert C.T.M. (2019). Computerized Stroop Tests: A Review. J Psychol Psychother 9:1. DOI: 10.4172/2161-0487.1000353.
Oyelade O.J., Oladipupo O.O., & Obagbuwa I.C. (2010). Application of k Means Clustering algorithm for prediction of Students Academic Performance. arXiv preprint arXiv:1002.2425.
Ozkok F.O., & Celik M. (2017). A new approach to determine Eps parameter of DBSCAN algorithm. International Journal of Intelligent Systems and Applications in Engineering, 5(4), 247-251.
Parkhi O.M., Vedaldi A. & Zisserman A. (2015). Deep Face Recognition. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 41.1-41.12. doi: 10.5244/C.29.41. isbn:1-901725-53-7
Parris B.A. (2014). Task conflict in the Stroop task: When Stroop interference decreases as Stroop facilitation increases in a low task conflict context. Frontiers in psychology, 5, 1182. (2014). https://doi.org/10.3389/fpsyg.2014.01182
Paul L.R., Sadath L., & Madana, A. (2021). Artificial Intellige nc e in Predictive Analysis of Insurance and Banking. In Artificial Intelligence (pp. 31-54). CRC Press.
Peña-Ayala A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications. Volume 41, Issue 4, Part 1, 2014, Pages 1432-1462, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2013.08.042.
Phaf R.H., & Rotteveel M. (2012). Affective monitoring: a generic mechanism for affect elicitation. Frontiers in psychology, 3, 47. https://doi.org/10.3389/fpsyg.2012.00047
Phaf R.H., Mohr S.E., Rotteveel, M., & Wicherts, J.M. (2014). Approach, avoidance, and affect: a meta-analysis of approach-avoidance tendencies in manual reaction time tasks. Frontiers in psychology, 5, 378. https://doi.org/10.3389/fpsyg.2014.00378
Piceci L., & Barbieri U. (2022). La motivazione nell'apprendimento in ambiente digitale attraverso l'Intelligenza Artificiale-Ubiquità Presenza Distanza. Journal of Inclusive Methodology and Technology in Learning and Teaching, 1(1).
Rahmah N., & Sitanggang, I.S. (2016). Determination of optimal epsilon (eps) value on dbscan algorithm to clustering data on peatland hotspots in sumatra. In IOP conference series: earth and environmental science (Vol. 31, No. 1, p. 012012). IOP Publishing.
Rolls E.T. (2004). The functions of the orbitofrontal cortex. Brain Cogn. 55(1):11-29. doi: 10.1016/S0278-2626(03)00277-X. PMID: 15134840.
Rolls E.T. (2019). The cingulate cortex and limbic systems for emotion, action, and memory. Brain Struct Funct 224, 3001–3018. https://doi.org/10.1007/s00429-019-01945-2
Romero C., & Ventura S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
Salamone J.D., Koob G., Le Moal M., Thompson R.F. (2010). Motor function and motivation (Eds.), Encyclopedia of Behavioral Neuroscience, Volume 3, Academic Press, Oxford (2010), pp. 267-276
Salamone J.D., Koychev I., Correa M., McGuire P. (2015). Neurobiological basis of motivational deficits in psychopathology. Eur Neuropsychopharmacol. 25(8):1225-38. doi: 10.1016/j.euroneuro.2014.08.014. Epub 2014 Sep 1. PMID: 25435083.
Scarpina F., & Tagini S. (2017). The Stroop Color and Word Test. Frontiers in psychology, 8, 557. https://doi.org/10.3389/fpsyg.2017.00557
Schipor M.D. & Schipor O. (2014). Motivation and Locus of Control: Relational Patterns Activated in Training for Teaching Career. Procedia - Social and Behavioral Sciences. 128. 420-425. 10.1016/j.sbspro.2014.03.181.
Seifert K., Sutton R. (2009). Educational Psychology. Published by the Saylor Foundation
Sinaga K.P., & Yang M.S. (2020). Unsupervised K-means clustering algorithm. IEEE access, 8, 80716-80727.
Spielberg J.M., Miller G.A., Warren S.L., Engels, A.S., Crocker L.D., Banich M.T., Sutton B.P., & Heller W. (2012). A brain network instantiating approach and avoidance motivation. Psychophysiology, 49(9), 1200–1214. https://doi.org/10.1111/j.1469-8986.2012.01443.x
Thomas M.S.C., Ansari D., Knowland V.C.P. (2018). Annual Research Review: Educational neuroscience: progress and prospects. J Child Psychol Psychiatry. Epub 2018 Oct 22. PMID: 30345518
Tran T. N., Drab K., & Daszykowski M. (2013). Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics and Intelligent Laboratory Systems, 120, 92-96.
Van Dessel P., Eder A.B., Hughes S. (2018). Mechanisms underlying effects of approach-avoidance training on stimulus evaluation. J Exp Psychol Learn Mem Cogn.44(8):1224-1241. doi: 10.1037/xlm0000514.
Wigfield A., Gladstone J., & Turci L. (2016). Beyond Cognition: Reading Motivation and Reading Comprehension. Child development perspectives, 10(3), 190–195. https://doi.org/10.1111/cdep.12184
Yin M., Wortman V.J., & Wallach H. (2019). Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 chi conference on human factors in computing systems (pp. 1-12).
Zhao L., Chen K., Song J., Zhu X., Sun J., Caulfield B., & Mac Namee B. (2020). Academic performance prediction based on multisource, multifeature behavioral data. IEEE Access, 9, 5453-5465.
DOI: https://doi.org/10.32043/gsd.v6i1.600
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Giornale Italiano di Educazione alla Salute, Sport e Didattica Inclusiva
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Italian Journal of Health Education, Sports and Inclusive Didactics
ISSN printed: 2532-3296