Online Homework Distraction ScaleA Validation Study
- Jianzhong Xu 1
- José Carlos Núñez 2
- Jennifer Cunha 3
- Pedro Rosário 3
-
1
Mississippi State University
info
-
2
Universidad de Oviedo
info
-
3
Universidade do Minho
info
ISSN: 0214-9915, 1886-144X
Año de publicación: 2020
Volumen: 32
Número: 4
Páginas: 469-475
Tipo: Artículo
Otras publicaciones en: Psicothema
Resumen
Background: Increasingly, postsecondary students enroll in distance learning courses and complete homework online, which extends their learning opportunities regardless of where they are. Online homework requires self-control from students to cope with conventional and tech-related distractors, however research on this topic is scarce. There is a need to develop an instrument to assess online homework distractions in higher education. Method: This study examined the psychometric properties of the Online Homework Distraction Scale (OHDS) based on 612 undergraduates in China. After randomly dividing the sample into two groups, we carried out a principal component analysis (PCA) with one group and confirmatory factor analysis (CFA) with another group. Results: Both PCA and CFA findings indicated that tech-related distraction and conventional distraction were empirically indistinguishable for college students. Given acceptable measurement invariance, the latent factor mean was examined over gender for all participants and found that men were more distracted while doing online homework. Concerning validity evidence, in line with theoretical predictions, the OHDS was negatively related to online homework expectancy, value, effort, and time management. Conclusions: Our study provides strong evidence that the OHDS is a valid and reliable instrument for measuring online homework distraction.
Referencias bibliográficas
- Bentler, P. M. (1995). EQS structural equations program manual. BMDP Statistical Software.
- Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: a perspective on assessment and intervention. Applied Psychology: An International Review, 54, 199-231. https://doi.org/10.1111/j.1464-0597.2005.00205.x
- Bowman, C. R., Gulacar, O., & King, D. B. (2014). Predicting Student Success via Online Homework Usage. Journal of Learning Design, 7(2), 47-61.
- Bowman, L. L., Levine, L. E., Waite, B. M., & Gendron, M. (2010). Can students really multitask? An experimental study of instant messaging while reading. Computers & Education, 54, 927-931. https://doi.org/10.1016/j.compedu.2009.09.024
- Calderón-Garrido, C., Navarro-González, D., Lorenzo-Seva, U., & Ferrando-Piera, P. J. (2019). Multidimensional or essentially unidimensional? A multi-faceted factor-analytic approach for assessing the dimensionality of tests and items. Psicothema, 31, 450-457. https://doi.org/10.7334/psicothema2019.153
- Chen F. F. (2007). Sensitivity of goodness offit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464-504. https://doi.org/10.1080/10705510701301834
- Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indices for testing measurement invariance. Structural Equation Modeling, 9, 233-255. https://doi.org/10.1207/S15328007SEM0902_5
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum.
- Cooper, H., Robinson, J. C., & Patall, E. A. (2006). Does homework improve academic achievement? A synthesis of research, 1987-2003. Review of Educational Research, 76, 1-62. https://doi.org/10.3102/00346543076001001
- Corbetta, M., Patel, G., & Shulman, G. L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58, 306-324. https://doi.org/10.1016/j.neuron.2008.04.017
- Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201-215. https://doi.org/10.1038/nrn755.
- Corno, L. (2004). Introduction to the special issue work habits and work styles: Volition in education. Teachers College Record, 106, 1669- 1694.
- David, P., Kim, J. H., Brickman, J. S., Ran, W., & Curtis, C. M. (2015). Mobile phone distraction while studying. New Media & Society, 17, 1661-1679. https://doi.org/10.1177/1461444814531692
- Eccles, J. S., & Wigfi eld, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132. https://doi.org/10.1146/annurev.psych.53.100901.135153
- Flanigan, A. E., & Babchuk, W. A. (2015). Social media as academic quicksand: A phenomenological study of student experiences in and out of the classroom. Learning and Individual Differences, 44, 40-45. https://doi.org/10.1016/j.lindif.2015.11.003
- Flunger, B., Trautwein, U., Nagengast, B., Lüdtke, O., Niggli, A., & Schnyder, I. (2017). A person-centered approach to homework behavior: Students’ characteristics predict their homework learning type. Contemporary Educational Psychology, 48, 1-15. https://doi.org/10.1016/j.cedpsych.2016.07.002
- Furst, R. T., Evans, D. N., & Roderick, N. M. (2018). Frequency of college student smartphone use: Impact on classroom homework assignments. Journal of Technology in Behavioral Science, 3, 49-57. https://doi.org/10.1007/s41347-017-0034-2
- Hancock, G. R. (1997). Structural equation modeling methods of hypothesis testing of latent variable means. Measurement and Evaluation in Counseling and Development, 30, 91-105.
- Hanson, T. L., Drumheller, K., Mallard, J., McKee, C., & Schlegel, P. (2010). Cell phones, text messaging, and Facebook: Competing time demands of today’s college students. College Teaching, 59, 23-30. https://doi.org/10.1080/87567555.2010.489078
- Hillstrom, A. P., & Chai, Y. C. (2006). Factors that guide or disrupt attentive visual processing. Computers in Human Behavior, 22, 648-656. https://doi.org/10.1016/j.chb.2005.12.003
- Hong, S., Malik, M. L., & Lee, M. (2003). Testing configural, metric, scalar, and latent mean invariance across genders in sociotropy and autonomy using a non-Western sample. Educational and Psychological Measurement, 63, 636-654. https://doi.org/10.1177/0013164403251332
- Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. https://doi.org/10.1080/10705519909540118
- Junco, R., & Cotten, S. R. (2011). Perceived academic effects of instant messaging use. Computers & Education, 56, 370-378. https://doi.org/10.1016/j.compedu.2010.08.020
- Kahneman, D. (1973). Attention and effort. Prentice Hall. Khanlarian, C., & Singh, R. (2015). Does technology affect student performance? Global Perspective on Accounting Education, 12, 1-22.
- Magalhães, P., Ferreira, D., Cunha, J., & Rosário, P. (2020). Online vs traditional homework: A systematic review on the benefits to students’ performance. Computers & Education, 152. https://doi.org/10.1016/j.compedu.2020.103869
- MacCallum, R. C., Brown, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149. http://dx.doi.org/10.1037/1082-989X.1.2.130
- Maruyama, G. M. (1998). Basics of structural equation modeling. Sage.
- Muñiz, J., & Fonseca-Pedrero, E. (2019). Ten steps for test development. Psicothema, 31, 7-16. https://doi.org/10.7334/psicothema2018.291
- Núñez, J. C., Suárez, N., Rosário, P., Vallejo, G., Cerezo, R., & Valle, A. (2015). Teachers’ feedback on homework, homework-related behaviors, and academic achievement. Journal of Educational Research, 108, 204-216. https://doi.org/10.1080/00220671.2013.878298
- Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill. Pashler, H., Johnston, J. C., & Ruthruff, E. (2001). Attention and performance. Annual Review of Psychology, 52, 629-651. https://doi.org/10.1146/annurev.psych.52.1.629
- Rao, N., Moely, B. E., & Sachs, J. (2000). Motivational beliefs, study strategies, and mathematics attainment in highand low-achieving Chinese secondary school students. Contemporary Educational Psychology, 25, 287-316. https://doi.org/10.1006/ceps.1999.1003
- Rosário, P., Núñez, J. C., Vallejo, G., Nunes, T., Cunha, J., Fuentes, S., & Valle, A. (2018). Homework purposes, homework behaviors, and academic achievement. Examining the mediating role of students’ perceived homework quality. Contemporary Educational Psychology, 53, 168- 180. https://doi.org/10.1016/j.cedpsych.2018.04.001
- Schmitz, B., & Wiese, B. S. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31, 64-96. https://doi.org/10.1016/j.cedpsych.2005.02.002
- Seaman, J. E., Allen, I. E., & Seaman, J. (2018). Grade increase: Tracking distance education in the United States. Babson Survey Research Group.
- Suárez-Álvarez, J., Pedrosa, I., Lozano, L. M., García-Cueto, E., Cuesta, M., & Muñiz, J. (2018). Using reversed items in likert scales: A questionable practice. Psicothema, 30, 149-158. https://doi.org/10.7334/psicothema2018.33
- Trehan, S., Sanzgiri, J., Li, C., Wang, R., & Joshi, R. M. (2017). Critical discussions on the Massive Open Online Course (MOOC) in India and China. International Journal of Education and Development using Information and Communication Technology, 13, 141-165
- Tsai, M. J. (2009). The model of strategic e-learning: Understanding and evaluating student e-learning from metacognitive perspectives. Journal of Educational Technology & Society, 12(1), 34-48.
- Watkins, M. W. (2017). The reliability of multidimensional neuropsychological measures: From alpha to omega. Clinical Neuropsychologist, 31(6-7), 1113-1126. https://doi.org/10.1080/13854046.2017.1317364
- Wolters, C. (2011). Regulation of motivation: Contextual and social aspects. Teachers College Record, 113, 265-283.
- Wu, J. Y., & Cheng, T. (2019). Who is better adapted in learning online within the personal learning environment? Relating gender differences in cognitive attention networks to digital distraction. Computers & Education, 128, 312-329. https://doi.org/10.1016/j.compedu.2018.08.016
- Xu, J. (2015). Investigating factors that influence conventional distraction and tech-related distraction in math homework. Computers & Education, 81, 304-314. https://doi.org/10.1016/j. compedu.2014.10.024
- Xu, J. (2018). Reciprocal effects of homework self-concept, interest, effort, and math achievement. Contemporary Educational Psychology, 55, 42- 52. https://doi.org/10.1016/j.cedpsych.2018.09.002
- Xu, J., Du, J., & Fan, X. (2013). “Finding our time”: Predicting students’ time management in online collaborative groupwork. Computers & Education, 69, 139-147. https://doi.org/10.1016/j.compedu.2013.07.012
- Xu, J., Fan, X., & Du, J. (2016). Homework Distraction Scale: Confirming the factor structure with middle school students. Journal of Psychoeducational Assessment, 34, 496-500. https://doi.org/10.1177/0734282915620900
- Xu, J., Fan, X., Du, J., & Cai, Z. (2019). Homework Expectancy Value Scale for undergraduates in online environments: Measurement invariance and latent mean differences across gender. European Journal of Psychological Assessment, 35, 666-673. https://doi.org/10.1027/1015-5759/a000455
- Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34, 33-42. https://doi.org/10.1016/j.lindif.2014.05.011
- Yushau, B., & Khan, M. A. (2014). Student perceptions of online homework in preparatory year pre-calculus courses. International Journal of Mathematics Trends and Technology, 8(1), 12-17.
- Zhou, Y., Chai, C. S., Liang, J. C., Jin, M., & Tsai, C. C. (2017). The relationship between teachers’ online homework guidance and technological pedagogical content knowledge about educational use of web. Asia-Pacific Education Researcher, 26, 239-247. https://doi.org/10.1007/s40299-017-0344-3