Development of the Individual and Peer Study Skills Scale

Yıl 2024, Cilt: 12 Sayı: 2, 888 - 924, 29.07.2024

Seval Fer , Levent Ertuna , İbrahim Uysal , Melih Derya Gürer , Murat Debbağ , Fatih Karataş , Derya Karadeniz , Yasemin Kuzgun , Esma Genç , İlker Cırık , Sevilay Yıldız , Hülya Pehlivan

https://doi.org/10.46778/goputeb.1460366

Öz

Learners can demonstrate the performance expected of them in study skills individually or with peers. The literature shows that there is no customization of study skills, such as working individually or with peers. Therefore, there is a need for measurement tools that can identify the needs of learners while determining their study skills for both individual and peer activities. This study aims to develop a scale to measure university students' individual and peer study skills. The research was conducted using an exploratory correlational design, and data was collected from two different samples for pilot and validation applications. The pilot and validation application sample comprised 470 and 323 teacher candidates. Item analysis for item validity and exploratory factor analysis (EFA) for construct validity were conducted on the pilot study data. Before the EFA, optimal parallel analysis was used to examine the scale's dimensionality. Confirmatory factor analysis (CFA) was conducted on the validation data to gather evidence for construct validity. The optimal parallel analysis suggested a two-dimensional structure for the scale. As a result of the EFA, a two-dimensional construct with 28 items, consisting of 16 and 12 items in each dimension, explained 58.8% of the variance. The first dimension of the scale was named peer study skills, and the second was named individual study skills. Item analysis revealed that the discrimination of the items in both dimensions was sufficient. The CFA results confirmed the two-factor construct of the scale. The trial and validation studies data showed that the reliability coefficients, considering both dimensions individually and the overall scale, indicated that the scores are reliable.

Anahtar Kelimeler

Individual Study Skills, Peer Study Skills, Scale Development, Preservice Teacher

Proje Numarası

122G041

Bireysel ve Akranla Çalışma Becerisi Ölçeğinin Geliştirilmesi

Öz

Öğrenenler, çalışma becerilerinde kendilerinden beklenen performansı bireysel veya akranlarıyla ortaya koyabilirler. Literatürdeki araştırmalarda çalışma becerilerine ilişkin bireysel veya akranla çalışma gibi bir özelleştirmeye gidilmediği görülmektedir. Bundan dolayı öğrenenlerin çalışma becerileri belirlenirken bireysel ve akranla çalışma etkinliklerine yönelik öğrenen ihtiyacını belirleyebilecek veri toplama araçlarına ihtiyaç vardır. Bu kapsamda çalışmanın amacı üniversite öğrencilerinin bireysel ve akranla çalışma becerilerini ölçmek amacıyla bir ölçek geliştirmektir. Açımlayıcı ilişkisel desende gerçekleştirilen araştırma kapsamında deneme ve geçerleme uygulamaları için iki farklı örneklemden veri toplanmıştır. Deneme uygulamasının örneklemini 470, geçerleme çalışmasının örneklemini 323 öğretmen adayı oluşturmuştur. Deneme uygulaması verileri üzerinde madde geçerliğine yönelik olarak madde analizi, yapı geçerliği için açımlayıcı faktör analizi (AFA) gerçekleştirilmiştir. Açımlayıcı faktör analizi öncesinde optimal paralel analiz kullanılarak ölçeğin boyutluluğu incelenmiştir. Geçerleme uygulaması verileri üzerinde doğrulayıcı faktör analizi (DFA) gerçekleştirilerek yapı geçerliğine yönelik kanıt toplanmıştır. Optimal paralel analiz sonucunda ölçek için iki boyutlu yapı önerilmiştir. AFA sonucunda 16 ve 12 maddeden oluşan toplamda 28 madde ile %58,8’lik varyans açıklama oranına sahip iki boyutlu bir ölçek yapısı elde edilmiştir. Ölçekteki birinci boyut akranla çalışma becerileri, ikinci boyut ise bireysel çalışma becerileri olarak isimlendirilmiştir. Madde analizi sonucunda her iki boyuttaki maddelerin ayırıcılığının yeterli düzeyde olduğu belirlenmiştir. DFA sonucunda ölçeğin belirlenen iki faktörlü yapısı doğrulanmıştır. Hem deneme hem geçerleme uygulaması verileri üzerinden elde edilen güvenirlik katsayıları incelendiğinde hem boyutlar hem de boyutlar dikkate alınarak ölçeğin bütünü incelendiğinde puanların güvenilir olduğu görülmüştür.

Anahtar Kelimeler

Bireysel Çalışma Becerisi, Akranla Çalışma Becerisi, Ölçek Geliştirme, Öğretmen Adayı

Destekleyen Kurum

TÜBİTAK

Proje Numarası

122G041

Kaynakça

• Abid, N., Aslam, S., Alghamdi, A.A., & Kumar, T. (2023). Relationships among students’ reading habits, study skills, and academic achievement in English at the secondary level. Frontiers in Psychology, 14, 1020269. https://doi.org/10.3389/fpsyg.2023.1020269
• Altman, D. G. (1991). Practical statistics for medical research. CRC.
• Ansari, Z. A. (1983). Study habits and attitude of students. Technical Report. National Institute of Psychology, Quaid-i-Azam University, Islamabad.
• Bandura, A. (1977). Social learning theory. Prentice-Hall.
• Bosworth, K. (1994). Developing collaborative skills in college students. In K. Bosworth & S. Hamilton (Eds.), Collaborative learning: Underlying processes and emerging practices, new directions for teaching and learning series, 59 (pp. 25-31). Jossey-Bass Publishers.
• Briggs, N. E., & MacCallum, R. C. (2003). Recovery of weak common factors by maximum likelihood and ordinary least squares estimation. Multivariate Behavioral Research, 38(1), 25-56. https://doi.org/10.1207/S15327906MBR3801_2
• Byrne, B. (2016). Structural equation modeling with Amos (3rd ed.). Routledge.
• Cooper, J.L., & Mueck, R. (1990). Student involvement in learning: Cooperative learning and college instruction. Journal of Excellence in College Teaching, 1(1), 68-76.
• Corrégé, J. B., & Michinov N. (2021). Group size and peer learning: Peer discussions in different group size influence learning in a biology exercise performed on a tablet with stylus. Frontiers in Education, 6(6), 1-13.
• Coughlin, K. B. (2013). An analysis of factor extraction strategies: A comparison of the relative strengths of principal axis, ordinary least squares, and maximum likelihood in research contexts that include both categorical and continuous variables [Doctoral dissertation, University of South Florida]. http://scholarcommons.usf.edu/etd/4459
• Creswell, J. (2015). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (5th ed.). Pearson.
• Daisy, P. J., & Radhakrishnan, N. (2018). Development and standardisation of study skills assessment scale. Journal of Management Research and Analysis, 5(2), 140-145. https://doi.org/10.18231/2394-2770.2018.0022
• Delphine, M., Sylvestre, N., Gabriel, N., & Wenceslas, N. (2022). A psychometric analysis of the Study Skills Questionnaire for University of Rwanda undergraduate students at National Police College. Creative Education, 13, 862-885. https://doi.org/10.4236/ce.2022.133057
• Deslauriers, L., Schelew, E., & Wieman, C. (2011). Improved learning in a large enrollment physics class. Science, 332, 862–864.
• Duncan, D. (2005). Clickers in the classroom: How to enhance science teaching using classroom response systems. Pearson/Addison-Wesley.
• Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266
• Entress, C., & Wagner, A. (2014). Beyond “hitting the books”: Teaching science students strategies for studying independently. The Science Teacher, 81(4), 27-31. https://www.jstor.org/stable/43746928
• Fazal, S. (2005). The relationship between study skills and academic achievement (Unpublished Master's thesis). Hazara Hazara University, Pakistan.
• Ferrando, P. J., & Anguiano-Carrasco, C. (2010). Factor analysis as a research technique in psychology. Papeles del Psicólogo, 31(1), 18-33.
• Finney, S. J., & DiStefano, C. (2013). Nonnormal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 439-492). IAP.
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
• Garson, G. D. (2023). Factor Analysis and Dimension Reduction in R: A Social Scientist's Toolkit. Taylor & Francis.
• Gholiazdeh, F. (2001). Correct methods of study & learning. Sahami Enteshar Co.
• Gwet, K. L. (2019). irrCAC: Computing Chance-Corrected Agreement Coefficients (CAC) (Version 1.0) [Computer software]. https://CRAN.R-project.org/package=irrCAC
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (9th ed.). Prentice-Hall.
• Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
• Hattie, J., Biggs, J., & Purdie, N. (1996). Effects of learning skills interventions on student learning: A meta-analysis. Review of Educational Research, 66(2), 99–136. https://doi.org/10.2307/1170605
• Hedin, B., & Kann, V. (2019). Improving study skills by combining a study skill module and repeated reflection seminars. Education Research International, 2019, 1–8. https://doi.org/10.1155/2019/9739854
• Herber, H. L. (1969). Reading in the content areas, study skill. In Herber, H. L. & Sanders, P. L. (Eds.), Reading to develop, remember and use ideas (pp. 13-22). Syracuse University.
• Hofer, B. K., & Yu, S. L. (2016). Teaching self-regulated learning through a “learning to learn” course. Teaching of Psychology, 30(1), 30–33. https://doi.org/10.1207/S15328023TOP3001_05
• Hoover, J. J., & Patton, J. R. (2007). Teaching study skills to students with learning problems: A teacher’s guide for meeting diverse needs. Pro-Ed.
• Howard, E., & Sarbaum, J. (2022). Addressing study skills, learning theory and critical thinking skills in principles of economics courses. Frontiers in Education, 7, 770464. https://doi.org/10.3389/feduc.2022.770464
• Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
• JASP Team (2023). JASP (Version 0.17.1) [Computer software]. https://jasp-stats.org/
• Kamp, R. J. A., Dolmans, D. H. J. M., van Berkel, H. J. M., & Schmidt, H. G. (2012). The relationship between students' small group activities, time spent on self-study, and achievement. Higher Education, 64(3), 385-397. https://doi.org/10.1007/s10734-011-9500-5
• Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford.
• Kooloos, J., van Kuppeveld, T., Bolhuis, S., & Vorstenbosch, M. (2016). The effect of in-class formality during a peer-teaching activity on student’s satisfaction, perceived participation and learning gain. Creative Education, 7, 1810-1819. http://dx.doi.org/10.4236/ce.2016.713184
• Kopzhassarova, U., Akbayeva, G., Eskazinova, Z., Belgibayeva, G., & Tazhikeyeva, A. (2016). Enhancement of students’ independent learning through their critical thinking skills development. International Journal of Environmental and Science Education, 11(18), 11585–11592.
• Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
• Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
• Liu, C.Y., & Chen, H. L. (2020). Effects of peer learning on learning performance, motivation, and attitude. International Journal of Education Economics and Development, 11(4), 420–443. http://dx.doi.org/10.1504/IJEED.2020.110599
• Lorenzo-Seva, U. (1999). Promin: A method for oblique factor rotation. Multivariate Behavioral Research, 34(3), 347-365. https://doi.org/10.1207/S15327906MBR3403_3
• Lorenzo-Seva, U., & Ferrando, P. J. (2022). Factor (Version 12.02.01) [Computer software]. Tarragona: Universitat Rovira i Virgili.
• Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519-530. https://doi.org/10.2307/2334770
• McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum.
• McLinden, M., & Edwards, C. (2011). Developing a culture of enquiry-based, independent learning in a research-led institution: findings from a survey of pedagogic practice. International Journal for Academic Development, 16(2), 147-162.
• Meyer, B., Haywood, N., Sachdev, D., & Faraday, S. (2008). Independent Learning Literature Review. DCSF (RR051).
• Meyers, L. S., Gamst, G., & Guarino, A. J. (2016). Applied multivariate research: Design and interpretation. Sage.
• Motevalli, S., Hamzah, M., Roslan, S., Hamzah, S., & Garmjani, M. (2021). The effects of study skills training on qualitative academic achievement among students. Asian Journal of University Education, 17(3),130-141. https://doi.org/10.24191/ajue.v17i3.14512
• Nájera Catalán, H. E. (2019). Reliability, population classification and weighting in multidimensional poverty measurement: A Monte Carlo study. Social Indicators Research, 142(3), 887-910. https://doi.org/10.1007/s11205-018-1950-z
• Piaget, J. (1972). Intellectual evolution from adolescence to adulthood. Human Development, 15(1), 1-12. https://doi.org/10.1159/000271225
• Pituch, K. A., & Stevens, J. P. (2016). Applied multivariate statistics for the social sciences: Analyses with SAS and IBM's SPSS (6th ed.). Routledge.
• Polkowski, Z., Jadeja, R., & Dutta, N. (2020). Peer learning in technical education and its worthiness: Some facts based on implementation. Procedia Computer Science, 172, 247–252. http://dx.doi.org/10.1016/j.procs.2020.05.039
• Porter, L., Bailey Lee, C., & Simon, B. (2013). Halving fail rates using peer instruction: a study of four computer science courses. In Proceedings of the 44th ACM technical symposium on Computer science education (pp. 177–182). Association for Computing Machinery. https://doi.org/10.1145/2445196.2445250
• Price, L. R. (2017). Psychometric methods: Theory into practice. Guilford.
• Revelle, W. (2023). Psych: Procedures for psychological, psychometric, and personality research (Version 2.3.9) [Computer software]. https://cran.r-project.org/package=psych
• Robitzsch, A. (2023). sirt: Supplementary Item Response Theory models (Version 3.13-228) [Computer software]. https://CRAN.R-project.org/package=sirt
• RStudio Team (2021). RStudio: Integrated development environment for R [Computer software]. http://www.rstudio.com
• Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864
• Shahidi, F., Dowlatkhah, H. R., Avand, A., Musavi, S. R., & Mohammadi, E. (2014). A study on the quality of study skills of newly-admitted students of Fasa University of Medical Sciences. Journal of Advances in Medical Education & Professionalism, 2(1), 45–50.
• The Jamovi Project (2023). Jamovi (Version 2.4.8) [Computer Software]. https://www.jamovi.org
• Trouche, E., Sander, E., & Mercier, H. (2014). Arguments, more than confidence, explain the good performance of reasoning groups. Journal of Experimental Psychology: General, 143, 1958–1971. https://doi.org/10.1037/a0037099
• Tullis, J. G., & Goldstone, R. L. (2020). Why does peer instruction benefit student learning?. Cognitive Research: Principles and Implications, 5(15). https://doi.org/10.1186/s41235-020-00218-5
• Utha, K., & Rinzin, S. (2019). Peer-learning: An alternative teaching pedagogy for highly teacher centered classes. International Journal of English, Literature and Social Science (IJELS), 4(5), 1520-1529. https://dx.doi.org/10.22161/ijels.45.41
• van den Hurk, M. M., Dolmans, D. H. J. M., Wolfhagen, I. H. A. P., vMuijtjens, A. M. M., & van der Vleuten, C. P. M. (1999). Impact of individual study on tutorial group discussion, Teaching and Learning in Medicine, 11(4), 196-201, https://doi.org/10.1207/S15328015TLM110403
• Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin ve E. Helmes (Ed.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy. Springer Science+Business Media.
• Vygotsky, L.S. (1978). Mind in society. Harvard University Press.
• West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). The Guilford Press.
• Wingate, U. (2006). Doing away with ‘study skills.’ Teaching in Higher Education, 11(4), 457–469. https://doi.org/10.1080/13562510600874268
• Yang-Wallentin, F., Jöreskog, K. G., & Luo, H. (2010). Confirmatory factor analysis of ordinal variables with misspecified models. Structural Equation Modeling: A Multidisciplinary Journal, 17(3), 392-423. http://dx.doi.org/10.1080/10705511.2010.489003