The European Educational Researcher

Paper-Based vs. Digitalized Glossaries in Laboratory Scripts

The European Educational Researcher, Volume 6, Issue 3, October 2023, pp. 79-99
OPEN ACCESS VIEWS: 392 DOWNLOADS: 193 Publication date: 15 Oct 2023
ABSTRACT
Abstract: In the future, learning will be essentially characterized by the ability to regulate the learning process and monitor success independently from a teacher. The technical possibilities offer better access to learning contents, precise and more individualized feedback, and learning phases adapted precisely to the needs of the learner in terms of scope and pace. In this study, we investigate an important aspect of the digitization of teaching/learning processes using the example of laboratory scripts for chemistry students at university. The focus is on looking up terms and concepts in preparation for the lab internships, firstly in a paper-based glossary and secondly in a digital glossary. During a two-day study, a total of 16 students prepared for experiments on two topics with completely identical materials. We then studied the influence of content knowledge, motivation, and cognitive load. While all students show significant learning achievements, there are no significant differences between the groups. Furthermore, results show that pure digitization of information has no effect, despite the theoretically assumed advantages.
KEYWORDS
Self-regulated learning, Digitalization, Laboratory scripts, Glossary
CITATION (APA)
Koenen, J., Mariot, L., & Tiemann, R. (2023). Paper-Based vs. Digitalized Glossaries in Laboratory Scripts. The European Educational Researcher, 6(3), 79-99. https://doi.org/10.31757/euer.632
REFERENCES
  1. Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help Seeking and Help Design in Interactive Learning Environments. Review of Educational Research, 73(3), 277–320. https://doi.org/10.3102/00346543073003277
  2. Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16(5), 389–400.
  3. Ayres, P., & van Gog, T. (2009). State of the art research into Cognitive Load Theory. Computers in Human Behavior, 25(2), 253–257.
  4. Baumert, J., Lüdtke, O., Trautwein, U., & Brunner, M. (2009). Large-scale student assessment studies measure the results of processes of knowledge acquisition: Evidence in support of the distinction between intelligence and student achievement. Educational Research Review, 4(3), 165–176. https://doi.org/10.1016/j.edurev.2009.04.002
  5. Bowen, W., Lack, K., Chingos, M,. & Nygren, T. (2015). Interactive Learning Online at Public Universities. New York. https://doi.org/10.18665/sr.22464
  6. Bühner, M. (2011). Einführung in die Test- und Fragebogenkonstruktion [Introduction to test and questionnaire construction]. PS Psychologie. München: Pearson Studium. Retrieved from http://ebooks.ciando.com/book/index.cfm/bok_id/203986;B:CIANDO
  7. Cattell, R. B. (1987). Intelligence: Its Structure, Growth and Action. Advances in psychology: Vol. 35. s.l.: Elsevier textbooks. Retrieved from http://site.ebrary.com/lib/alltitles/docDetail.action?docID=10259213
  8. Chandler, P., & Sweller, J. (1991). Cognitive Load Theory and the Format of Instruction. Cognition and Instruction, 8(4), 293–332. https://doi.org/10.1207/s1532690xci0804_2
  9. Choi, H.-H., van Merriënboer, J. J. G. & Paas, F. (2014). Effects of the Physical Environment on Cognitive Load and Learning: Towards a New Model of Cognitive Load. Educational Psychology Review, 26(2), 225-244. https://doi.org/10.1007/s10648-014-9262-6
  10. Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load?. Computers in Human Behavior, 25(2), 315–324.
  11. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114. https://doi.org/10.1017/S0140525X01003922
  12. Deci, E.& Ryan, R. (1993). Die Selbstbestimmungstheorie der Motivation und ihre Bedeutung für die Pädagogik [The self-determination theory of motivation and ist importance for pedagogy]. Zeitschrift für Pädagogik, 39 (2), 223-238.
  13. De Jong, T. (2010). Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science, 38(2), 105-134. https://doi.org/10.1007/s11251-009-9110-0
  14. Dunn, D. S. (2013). Research Methods for Social Psychology. Hoboken: Wiley.
  15. Dunn, K. (2014). Why Wait? The Influence of Academic Self-Regulation, Intrinsic Motivation, and Statistics Anxiety on Procrastination in Online Statistics. Innovative Higher Education, 39(1), 33-44. https://doi.org/10.1007/s10755-013-9256-1
  16. Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. and sex and drugs and rock 'n' roll. Los Angeles, London, New Dehli, Singapore, Washington DC: SAGE Publications.
  17. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906-9011. https://doi.org/10.1037/0003-066X.34.10.906
  18. Galloway, K. R., Malakpa, Z., & Bretz, S. L. (2016). Investigating affective experiences in the undergraduate chemistry laboratory: students` perception of control and resposibility. Journal of chemical education, 39, 227-238.
  19. Gokhale, A. A. (1995). Collaborative Learning Enhances Critical Thinking. Journal of Technology Education, 7(1), 22–30.
  20. Göpferich, S. (1998). Interkulturelles Technical Writing: Fachliches adressatengerecht vermitteln; ein Lehr- und Arbeitsbuch [Intercultural technical writing: conveying technical information in a way that is appropriate to the target audience; a textbook and workbook]. Forum für Fachsprachen-Forschung: Vol. 40. Tübingen: Narr.
  21. Gretsch, P., & Holzäpfel, L. (Eds.). (2016). Lernen mit Visualisierungen: Erkenntnisse aus der Forschung und deren Implikationen für die Fachdidaktik [Learning with visualizations: Findings from research and their implications for subject didactics]. Münster, New York: Waxmann Verlag. Retrieved from http://ebooks.ciando.com/book/index.cfm/bok_id/2180772
  22. Heckhausen, J., & Heckhausen, H. (Eds.). (2006). Motivation und handeln [Motivation and action]. Springer-Verlag
  23. Heller, K. A., & Perleth, C. (2000). Kognitiver Fähigkeits-Test (Rev.) für 5.-12. Klasse (KFT 5-12+R) [Cognitive Ability Test (Rev.) for grades 5-12 Class (KFT 5-12+R)]. Göttingen: Beltz-Testgesellschaft.
  24. Hucke, L. (2000). Handlungsregulation und Wissenserwerb in traditionellen und computergestützten Experimenten des physikalischen Praktikums [Action regulation and knowledge acquisition in traditional and computer-supported experiments in physics internships]. Berlin: Logos.
  25. IBM Corp. Released 2021. IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp
  26. Kalyuga, S. (2007). Enhancing Instructional Efficiency of Interactive E-learning Environments: A Cognitive Load Perspective. Educational Psychology Review, 19(3), 387–399. https://doi.org/10.1007/s10648-007-9051-6
  27. Kerres, M. (2001). Multimediale und telemediale Lernumgebungen: Konzeption und Entwicklung [Multimedia and telemedia learning environments: conception and development]. München, Wien: Oldenbourg.
  28. Leppink, J., Paas, F., van der Vleuten, C. P. M., van Gog, T., & van Merriënboer, J. J. G. (2013). Development of an instrument for measuring different types of cognitive load. Behavior Research Methods, 45(4), 1058–1072.
  29. Mayer, R. E. (2009). Multimedia learning. New York NY u.a.: Cambridge Univ. Press.
  30. Mayer, R. E. & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43-52. https://doi.org/10.1207/S15326985EP3801_6
  31. Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological review, 63(2), 81–97.
  32. Niedderer, H., Tiberghien, A., Haller, K., Hucke, L., Sander, F. & Fischer, H. (2003). Talking Physics in Labwork Contexts. A Category Based Analysis of Videotapes. In D. Psillos & H. Niedderer (Ed.), Teaching and Learning in the Science Laboratory (Vol. 16, pp. 31–40). Dordrecht: Kluwer Academic Publishers.
  33. Nunnally, J. C. (1967). Psychometric theory. New York: McGraw-Hill.
  34. Paas, F., & Sweller, J. (2014). Implications of Cognitive Load Theory for Multimedia Learning. In R. E. Mayer (Ed.), Cambridge handbooks in psychology. The Cambridge handbook of multimedia learning (pp. 27–42). Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139547369.004
  35. Paas, F., & van Merriënboer, J. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86(1), 122–133. https://doi.org/10.1037/0022-0663.86.1.122
  36. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist, 38(1), 1–4.
  37. Paas, F., Tuovinen, J. E., van Merriënboer, J., & Aubteen Darabi, A. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology Research and Development, 53(3), 25–34. https://doi.org/10.1007/BF02504795
  38. Paivio, A. (1986). Mental representations: A dual coding approach. Oxford psychology series: Vol. 9. New York NY u.a.: Oxford Univ. Press.
  39. Puentedura, R. (2006). Transformation, technology, and education [Blog post]. Retrieved from http://hippasus.com/resources/tte/.
  40. Reeves, T. C., Herrington, J., & Oliver, R. (2004). A development research agenda for online collaborative learning. Educational Technology Research and Development, 52(4), 53-65.
  41. Renkl, A., & Atkinson, R. K. (2003). Structuring the Transition From Example Study to Problem Solving in Cognitive Skill Acquisition: A Cognitive Load Perspective. Educational Psychologist, 38(1), 15–22.
  42. Rheinberg, F., Vollmeyer, R., & Burns, B. D. (2001). FAM: Ein Fragebogen zur Erfassung aktueller Motivation in Lern- und Leistungssituationen [FAM: A questionnaire to record current motivation in learning and performance situations]. Diagnostica, 48, 57–66.
  43. Robson, C. (2002). Real world research. Oxford: Blackwell Publishing.
  44. Salomon, G. (1984). Television is "easy" and print is "tough": The differential investment of mental effort in learning as a function of perceptions and attributions. Journal of Educational Psychology, 76(4), 647–658.
  45. Schmeck, A., Opfermann, M., van Gog, T., Paas, F., & Leutner, D. (2015). Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings. Instructional Science, 43(1), 93–114. https://doi.org/10.1007/s11251-014-9328-3
  46. Schüßler, K. (2016). Lernen mit Lösungsbeispielen im Chemieunterricht. Einflüsse auf Lernerfolg, kognitive Belastung und Motivation [Learning with solution examples in chemistry lessons. Influences on learning success, cognitive load and motivation]. Universität Duisburg-Essen, Duisburg.
  47. Seery, M. K., Agustian, H. Y. & Zhang, X. (2019). A framework for learning in the chemistry laboratory. Israel Journal of Chemistry, 59, 546-553. https://doi.org/10.1002/ijch.201800093
  48. Skulmowski, A. & Xu, K. M. (2022). Understanding Cognitive Load in Digital and Online Learning: a New Perspective on Extraneous Cognitive Load. Educational Psychology Review, 34(1), 171-196. https://doi.org/10.1007/s10648-021-09624-7
  49. Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
  50. Sweller, J. (2004). Instructional Design Consequences of an Analogy between Evolution by Natural Selection and Human Cognitive Architecture. Instructional Science, 32(1/2), 9–31. https://doi.org/10.1023/B:TRUC.0000021808.72598.4d
  51. Sweller, J., Ayres, P., Kalyuga, S., & Chandler, P. (2003). The Expertise Reversal Effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4
  52. Sweller, J., & Sweller, S. (2006). Natural Information Processing Systems. Evolutionary Psychology, 4(1), 147470490600400. https://doi.org/10.1177/147470490600400135
  53. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York. Springer.
  54. Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251–296. https://doi.org/10.1023/A:1022193728205
  55. van Gog, T., Ericsson, K. A., Rikers, R. M. J. P., & Paas, F. (2005). Instructional design for advanced learners: Establishing connections between the theoretical frameworks of cognitive load and deliberate practice. Educational Technology Research and Development, 53(3), 73–81. https://doi.org/10.1007/BF02504799
  56. van Gog, T., Kirschner, F., Kester, L., & Paas, F. (2012). Timing and Frequency of Mental Effort Measurement: Evidence in Favour of Repeated Measures. Applied Cognitive Psychology, 26(6), 833–839. https://doi.org/10.1002/acp.2883
  57. Wallen, E., Plass, J. L., & Brünken, R. (2005). The function of annotations in the comprehension of scientific texts: Cognitive load effects and the impact of verbal ability. Educational Technology Research and Development, 53(3), 59–71. https://doi.org/10.1007/BF02504798
  58. Weiner, B. (1986). An attributional theory of motivation and emotion. Springer Series in Social Psychology
  59. Zhang, L., Kirschner, P. A., Cobern, W. W., & Sweller, R. (2022). There is an evidence crisis in science education policy. Educational Psychology Review, 34, 1157-1176. https://doi.org/10.1007/s10648-021-09646-1
LICENSE
Creative Commons License