Academic achievement prediction in secondary education by decision tree analysis

Israel Villarrasa-Sapiña; Xavier García-Massó; Encarnación Liébana; Gonzalo Monfort Torres

doi:10.5944/educxx1.33351

Authors

Israel Villarrasa-Sapiña Universidad Internacional de La Rioja, Spain https://orcid.org/0000-0002-2831-1033
Xavier García-Massó Universitat de València, Spain https://orcid.org/0000-0002-5925-4537
Encarnación Liébana Universitat Católica de València, Spain https://orcid.org/0000-0002-2617-4549
Gonzalo Monfort Torres Universitat Politècnica de València, Spain https://orcid.org/0000-0001-8275-2238

DOI:

https://doi.org/10.5944/educxx1.33351

Keywords:

high schools, academic achievement, prediction, physical activity level, decision tree

Abstract

The aim of the present study was to develop a predictive model of academic achievement (school success or failure) by applying a decision tree analysis. A cross-sectional study was carried out to design a system for the early detection of academic failure. 219 adolescents (aged 14 to 16) participated and information on their socioeconomic status, body mass index (BMI) percentile, physical activity, leisure time spent in front of screens, enjoyment, hope, anger, anxiety, boredom, behavioral engagement, emotional engagement, cognitive engagement, self-perceived school performance and intention to go to university was collected as input variables in decision tress analysis. 6 failure and 3 success groups were found able to predict academic performance. Good accuracy was obtained in the training (80.11 %) and validation (81.40 %) datasets of the decision tree. It is possible to predict academic failure or success by assessing weight status, physical activity, anger and hope during school attendance, intention to go to university and self-perceived school performance.

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References

Alexander, K. L., Entwisle, D. R., & Horsey, C. S. (1997). From first grade forward: early foundations of high school dropout. Sociology of Education, 70(2), 87–107. https://doi.org/10.2307/2673158

Alexander, K. L., Entwisle, D. R., & Kabbani, N. S. (2001). The dropout process in life course perspective: early risk factors at home and school. Teachers College Record, 103(5), 760–822. https://doi.org/10.1111/0161-4681.00134

Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7

Alzina, R. B., & Escoda, N. P. (2012). Educación emocional: estrategias para su puesta en práctica. Avances en Supervisión Educativa, 16, 16. https://avances.adide.org

Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Scientific Research Journal for Engineering, Technology, and Sciences, 44(1), 1.

Cairns, R. B., Cairns, B. D., & Neckerman, H. J. (1989). Early school dropout: configurations and determinants. Child Development, 60(6), 1437–1452. https://doi.org/10.2307/1130933

Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: testing the linkages. Research in Higher Education, 47(1), 1–32. https://doi.org/10.1007/s11162-005-8150-9

Casillas, A., Robbins, S., Allen, J., Kuo, Y.-L., Hanson, M. A., & Schmeiser, C. (2012). Predicting early academic failure in high school from prior academic achievement, psychosocial characteristics, and behavior. Journal of Educational Psychology, 104(2), 407–420. https://doi.org/10.1037/a0027180

Coe, D. P., Peterson, T., Blair, C., Schutten, M. C., & Peddie, H. (2013). Physical fitness, academic achievement, and socioeconomic status in school-aged youth. The Journal of School Health, 83(7), 500–507. https://doi.org/10.1111/josh.12058

Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and relatedness: a motivational analysis of self-system processes. In M. R. Gunnar & L. A. Sroufe (Eds.) Self processes and development (pp. 43–77). Lawrence Erlbaum Associates, Inc.

Csikszentmihalyi, M. (2014). Toward a psychology of optimal experience. En M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology: the collected works of mihaly csikszentmihalyi (pp. 209–226). Springer Netherlands. https://doi.org/10.1007/978-94-017-9088-8_14

Currie, C., Molcho, M., Boyce, W., Holstein, B., Torsheim, T., & Richter, M. (2008). Researching health inequalities in adolescents: the development of the Health Behaviour in School-Aged Children (HBSC) family affluence scale. Social Science & Medicine, 66(6), 1429–1436. https://doi.org/10.1016/j.socscimed.2007.11.024

Davis, A., Solberg, V. S., Baca, C. de, & Gore, T. H. (2014). Use of social emotional learning skills to predict future academic success and progress toward graduation. Journal of Education for Students Placed at Risk, 19(3–4), 169–182. https://doi.org/10.1080/10824669.2014.972506

Díaz, E. R., Fernández, A. R., & Revuelta, L. R. (2016). Validation of the Spanish version of the School Engagement Measure (SEM). The Spanish Journal of Psychology, 19, 85–89.

Dixson, D. D. (2019). Hope into action: how clusters of hope relate to success-oriented behavior in school. Psychology in the Schools, 56(9), 1493–1511. https://doi.org/10.1002/pits.22299

D’Mello, S., Jackson, G., Craig, S., Morgan, B., Chip-Man, P., White, H., Person, N., Kort, B., Kaliouby, R., Picard, R., & Graesser, A. (2008). AutoTutor detects and responds to learners affective and cognitive states. Workshop on emotional and cognitive issues at the International Conference Intelligent Tutoring Systems. Montreal, Canada.

Doğan, U. (2014). Validity and reliability of Student Engagement Scale. Bartın University Journal of Faculty of Education, 3(2), 390-403. https://doi.org/10.14686/BUEFAD.201428190

Dogan, U. (2015). Student engagement, academic self-efficacy, and academic motivation as predictors of academic performance. The Anthropologist, 20(3), 553–561. https://doi.org/10.1080/09720073.2015.11891759

Eisenberg, N., Damon, W., & Lerner, R. M. (2006). Handbook of child psychology: social, emotional, and personality development (6th ed). John Wiley & Sons, Inc.

Enguita, M. F., Martínez, L. M., & Gómez, J. R. (2010). Fracaso y abandono escolar en España. Fundación la Caixa.

Feldman, D. B., & Kubota, M. (2015). Hope, self-efficacy, optimism, and academic achievement: distinguishing constructs and levels of specificity in predicting college grade-point average. Learning and Individual Differences, 37, 210–216. https://doi.org/10.1016/j.lindif.2014.11.022

Fernandez-Lasarte, O., Goñi, E., Camino, I., & Zubeldia, M. (2019). Ajuste escolar y autoconcepto académico en la Educación Secundaria. Revista de Investigación Educativa, 37(1), 1. https://doi.org/10.6018/rie.37.1.308651

Festinger, L. (1962). A theory of Cognitive Dissonance. Stanford University Press.

Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59(2), 117–142. https://doi.org/10.3102/00346543059002117

Finn, K. E., Faith, M. S., & Seo, Y. S. (2018). School engagement in relation to body mass index and school achievement in a high-school age sample. Journal of Obesity, 2018, 3729318. https://doi.org/10.1155/2018/3729318

Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: a comparative analysis of various methods and student self-report instruments. En S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). Springer US. https://doi.org/10.1007/978-1-4614-2018-7_37

García-Escalera, J., Valiente, R. M., Sandín, B., Ehrenreich-May, J., & Chorot, P. (2020). Los efectos de un programa de prevención de la ansiedad y la depresión para adolescentes en variables educativas y de bienestar. Revista de Psicodidáctica, 25(2), 143–149. https://doi.org/10.1016/j.psicod.2020.05.001

Godoy, J. A., Abrahão, R. C., & Halpern, R. (2013). Autopercepção de dificuldades escolares em alunos do ensino fundamental e médio em município do Rio Grande do Sul. Aletheia, 41, 121–133.

Graesser, A. C., & Olde, B. A. (2003). How does one know whether a person understands a device? The quality of the questions the person asks when the device breaks down. Journal of Educational Psychology, 95(3), 524–536. https://doi.org/10.1037/0022-0663.95.3.524

Hardy, L. L., Booth, M. L., & Okely, A. D. (2007). The reliability of the Adolescent Sedentary Activity Questionnaire (ASAQ). Preventive Medicine, 45(1), 71–74. https://doi.org/10.1016/j.ypmed.2007.03.014

Hillman, C. H., Castelli, D. M., & Buck, S. M. (2005). Aerobic fitness and neurocognitive function in healthy preadolescent children. Medicine and Science in Sports and Exercise, 37(11), 1967–1974. https://doi.org/10.1249/01.mss.0000176680.79702.ce

Kowalski, K. C., Crocker, P. R. E., & Kowalski, N. P. (1997). Convergent validity of the Physical Activity Questionnaire for Adolescents. Pediatric Exercise Science, 9(4), 342–352. https://doi.org/10.1123/pes.9.4.342

Kuczmarski, R., Ogden, C., Grummer-Strawn, L., Flegal, K., Shumei, G., Wei, R., Mei, Z., Curtin, L., Roche, A., & Johnson, C. (2000). CDC growth charts: United States. National Center for Health Statistics, 314, 28.

Lane, A. M., Whyte, G. P., Terry, P. C., & Nevill, A. M. (2005). Mood, self-set goals and examination performance: the moderating effect of depressed mood. Personality and Individual Differences, 39(1), 143–153. https://doi.org/10.1016/j.paid.2004.12.015

Marques, A., Corrales, F. R. G., Martins, J., Catunda, R., & Sarmento, H. (2017). Association between physical education, school-based physical activity, and academic performance: a systematic review. Retos: Nuevas Tendencias En Educación Física, Deporte y Recreación, 31, 316–320.

Martínez García, J. S. (2011). Género y origen social: diferencias grandes en fracaso escolar administrativo y bajas en rendimiento educativo. Revista de Sociología de la Educación-RASE, 4(3), 270–282.

Martínez-Gómez, D., Martínez-de-Haro, V., Pozo, T., Welk, G. J., Villagra, A., Calle, M. E., Marcos, A., & Veiga, O. L. (2009). Fiabilidad y validez del cuestionario de actividad física PAQ-A en adolescentes españoles. Revista Española de Salud Pública, 83(3), 427–439.

Morales, J., González, L.-M., Guerra, M., Virgili, C., & Unnithan, V. (2011). Physical activity, perceptual-motor performance, and academic learning in 9-to-16-years-old school children. International Journal of Sport Psychology, 42(4), 401–415.

Morales, J., Pellicer-Chenoll, M., García-Masso, X., Gomis, M., & González, L.-M. (2011). Relation between physical activity and academic performance in 3rd-year secondary education students. Perceptual and Motor Skills, 113(2), 539–546.

Moreira, B. B. G., Martins-Reis, V. de O., & Santos, J. N. (2016). Autopercepção das dificuldades de aprendizagem de estudantes do ensino fundamental. Audiology — Communication Research, 21. https://doi.org/10.1590/2317-6431-2015-1632

Parker, J. D. A., Summerfeldt, L. J., Hogan, M. J., & Majeski, S. A. (2004). Emotional intelligence and academic success: examining the transition from high school to university. Personality and Individual Differences, 36(1), 163–172. https://doi.org/10.1016/S0191-8869(03)00076-X

Parr, A. K., & Bonitz, V. S. (2015). Role of family background, student behaviors, and school-related beliefs in predicting high school dropout. The Journal of Educational Research, 108(6), 504–514. https://doi.org/10.1080/00220671.2014.917256

Peiró-Velert, C., Valencia-Peris, A., González, L. M., García-Massó, X., Serra-Añó, P., & Devís-Devís, J. (2014). Screen media usage, sleep time and academic performance in adolescents: clustering a self-organizing maps analysis. Plos One, 9(6), e99478. https://doi.org/10.1371/journal.pone.0099478

Pekrun, R. (2006). The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

Pekrun, R., Goetz, T., Daniels, L. M., Stupnisky, R. H., & Perry, R. P. (2010). Boredom in achievement settings: exploring control–value antecedents and performance outcomes of a neglected emotion. Journal of Educational Psychology, 102(3), 531–549. https://doi.org/10.1037/a0019243

Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: the Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002

Pekrun, R., Goetz, T., Titz, W., & Perry, R. (2002). Academic emotions in students’ self-regulated learning and achievement: a program of qualitative and quantitative research. Educational Psychologist, 37, 91–105. https://doi.org/10.1207/S15326985EP3702_4

Pellicer-Chenoll, M., Garcia-Massó, X., Morales, J., Serra-Añó, P., Solana-Tramunt, M., González, L.-M., & Toca-Herrera, J.-L. (2015). Physical activity, physical fitness and academic achievement in adolescents: a self-organizing maps approach. Health Education Research, 30(3), 436–448. https://doi.org/10.1093/her/cyv016

Peterson, J. L., Puhl, R. M., & Luedicke, J. (2012). An experimental assessment of physical educators’ expectations and attitudes: the importance of student weight and gender. Journal of School Health, 82(9), 432–440. https://doi.org/10.1111/j.1746-1561.2012.00719.x

Quadri, M. M. N., & Kalyankar, D. N. V. (2010). Drop out feature of student data for academic performance using decision tree techniques. Global Journal of Computer Science and Technology, 2(10). https://globaljournals.org/GJCST_Volume10/gjcst_vol10_issue2_ver1_paper7.pdf

Rasberry, C. N., Lee, S. M., Robin, L., Laris, B. A., Russell, L. A., Coyle, K. K., & Nihiser, A. J. (2011). The association between school-based physical activity, including physical education, and academic performance: a systematic review of the literature. Preventive Medicine, 52, S10–S20. https://doi.org/10.1016/j.ypmed.2011.01.027

Respondek, L., Seufert, T., Stupnisky, R., & Nett, U. E. (2017). Perceived academic control and academic emotions predict undergraduate university student success: examining effects on dropout intention and achievement. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00243

Rokach, L., & Maimon, O. (2014). Data mining with decision trees: theory and applications (2ª ed.). World Scientific Publishing Co., Inc.

Rosas, J. S. (2015). The Achievement Emotions Questionnaire-Argentine (AEQ-AR): internal and external validity, reliability, gender differences and norm-referenced interpretation of test scores. Revista Evaluar, 15(1), 1. https://doi.org/10.35670/1667-4545.v15.n1.14908

Rubia, J. C. M. de la, Sosa, J. C. S., & González, M. E. V. (2010). Desarrollo de una escala multidimensional breve de ajuste escolar. Revista Electrónica de Metodología Aplicada, 15(1),1. https://doi.org/10.17811/rema.15.1.2010.1-11

Sallis, J. F., McKenzie, T. L., Kolody, B., Lewis, M., Marshall, S., & Rosengard, P. (1999). Effects of health-related physical education on academic achievement: Project SPARK. Research Quarterly for Exercise and Sport, 70(2), 127–134. https://doi.org/10.1080/02701367.1999.10608030

Shaw, S. R., Gomes, P., Polotskaia, A., & Jankowska, A. M. (2015). The relationship between student health and academic performance: implications for school psychologists. School Psychology International, 36(2), 115–134. https://doi.org/10.1177/0143034314565425

Sherry, B., Jefferds, M. E., & Grummer-Strawn, L. M. (2007). Accuracy of adolescent self-report of height and weight in assessing overweight status: a literature review. Archives of Pediatrics & Adolescent Medicine, 161(12), 1154–1161. https://doi.org/10.1001/archpedi.161.12.1154

Sigfúsdóttir, I. D., Kristjánsson, A. L., & Allegrante, J. P. (2007). Health behaviour and academic achievement in Icelandic school children. Health Education Research, 22(1), 70–80. https://doi.org/10.1093/her/cyl044

Singh, A. S., Saliasi, E., Berg, V. van den, Uijtdewilligen, L., Groot, R. H. M. de, Jolles, J., Andersen, L. B., Bailey, R., Chang, Y.-K., Diamond, A., Ericsson, I., Etnier, J. L., Fedewa, A. L., Hillman, C. H., McMorris, T., Pesce, C., Pühse, U., Tomporowski, P. D., & Chinapaw, M. J. M. (2019). Effects of physical activity interventions on cognitive and academic performance in children and adolescents: a novel combination of a systematic review and recommendations from an expert panel. British Journal of Sports Medicine, 53(10), 640–647. https://doi.org/10.1136/bjsports-2017-098136

Singh, A., Uijtdewilligen, L., Twisk, J. W. R., van Mechelen, W., & Chinapaw, M. J. M. (2012). Physical activity and performance at school: a systematic review of the literature including a methodological quality assessment. Archives of Pediatrics & Adolescent Medicine, 166(1), 49–55. https://doi.org/10.1001/archpediatrics.2011.716

Skinner, E. A., Wellborn, J. G., & Connell, J. P. (1990). What it takes to do well in school and whether I’ve got it: a process model of perceived control and children’s engagement and achievement in school. Journal of Educational Psychology, 82(1), 22–32. https://doi.org/10.1037/0022-0663.82.1.22

Solberg, V. S., Gusavac, N., Hamann, T., Felch, J., Johnson, J., Lamborn, S., & Torres, J. (1998). The Adaptive Success Identity Plan (ASIP): a career intervention for college students. The Career Development Quarterly, 47(1), 48–95. https://doi.org/10.1002/j.2161-0045.1998.tb00728.x

Sullivan, R. A., Kuzel, A. H., Vaandering, M. E., & Chen, W. (2017). The association of physical activity and academic behavior: a systematic review. Journal of School Health, 87(5), 388–398. https://doi.org/10.1111/josh.12502

Titz, W. (2001). Emotionen von studierenden in lernsituationen: explorative analysen und entwicklung von selbstberichtskalen — anhang. https://epub.uni-regensburg.de/9862/

Tomporowski, P. D., Davis, C. L., Miller, P. H., & Naglieri, J. A. (2008). Exercise and children’s intelligence, cognition, and academic achievement. Educational Psychology Review, 20(2), 111–131. https://doi.org/10.1007/s10648-007-9057-0

Trujillo-Torres, J.-M., Hossein-Mohand, H., Gómez-García, M., Hossein-Mohand, H., & Hinojo-Lucena, F.-J. (2020). Estimating the academic performance of secondary education mathematics students: a gain lift predictive model. Mathematics, 8(12), 12. https://doi.org/10.3390/math8122101

Vairachilai S, V. (2020). Student’s academic performance prediction using machine learning approach. International Journal of Advanced Science and Technology, 29, 9s.

Van Dusen, D. P., Kelder, S. H., Kohl, H. W., Ranjit, N., & Perry, C. L. (2011). Associations of physical fitness and academic performance among schoolchildren. The Journal of School Health, 81(12), 733–740. https://doi.org/10.1111/j.1746-1561.2011.00652.x

van Praag, H. (2009). Exercise and the brain: something to chew on. Trends in Neurosciences, 32(5), 283–290. https://doi.org/10.1016/j.tins.2008.12.007

Veitch, W. R. (11-14 de abril de 2004). Identifying characteristics of high school dropouts: data mining with a decision tree model [Ponencia de Congreso]. Annual Meeting of the American Educational Research Association, San Diego. https://eric.ed.gov/?id=ED490086

Weiner, B. (1982). An attribution theory of motivation and emotion. Series in Clinical & Community Psychology: Achievement, Stress, & Anxiety, 223–245.

Wittberg, R. A., Northrup, K. L., & Cottrel, L. (2009). Children’s physical fitness and academic performance. American Journal of Health Education, 40(1), 30–36.

York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research & Evaluation, 20(5). https://eric.ed.gov/?id=EJ1059739

Yu, L. C., Lee, C. W., Pan, H. I., Chou, C. Y., Chao, P. Y., Chen, Z. H., Tseng, S. F., Chan, C. L., & Lai, K. R. (2018). Improving early prediction of academic failure using sentiment analysis on self-evaluated comments. Journal of Computer Assisted Learning, 34(4), 358–365. https://doi.org/10.1111/jcal.12247

Zhang, X., Xue, R., Liu, B., Lu, W., & Zhang, Y. (28-30 de julio de 2018). Grade prediction of student academic performance with multiple classification models. [Ponencia de Congreso]. 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), China. https://doi.org/10.1109/FSKD.2018.8687286