Procedure for the assessment of cognitive complexity: Development and implementation in the topic "Hydrolysis of salts"

Saša Antal Horvat, Jovana Mihajlović, Tamara Rončević, Dušica Rodic

Abstract


The aim of this research was the creation and validation of a procedure for determining the cognitive complexity of problem tasks in the field of salt hydrolysis. The procedure created included an assessment of the difficulty of concepts and an assessment of their interactivity. One of the research tasks was to determine whether there were misconceptions by students that might have influenced their achievement. There were 50 Bachelor of Science in Chemistry students who participated in the study. A knowledge test was used as a research instrument to assess the performance and a seven-point Likert scale to evaluate the invested mental effort. The validity of this instrument for the assessment of cognitive complexity was confirmed by a series of regression analyses, where acceptable and statistically significant correlation coefficients were obtained among the examined variables: student performance and invested mental effort as dependent variables and cognitive complexity as independent variable

Keywords


Mental effort; Performance; Cognitive complexity; Salt hydrolysis

Full Text:

PDF

References


D. P. Cartrette, P. M. Mayo, Students' understanding of acids/bases in organic chemistry contexts. Chem. Educ. Res. Prac., 12, 29–39 (2011).

DOI: 10.1039/C1RP90005F

J. M. Nyachwaya, General chemistry students' conceptual understanding and language fluency: acid-base neutraliza-tion and conductometry. Chem. Educ. Res. Pract., 17, 509–522, (2016). DOI: 10.1039/C6RP00015K

C. Alvarado, F. Canada, A. Garritz, V. Mellado, Canoni-cal Pedagogical Content Knowledge by CoRes for Teach-ing Acid-Base Chemistry at High School. Chem. Educ. Res. Pract., 16, 603–618 (2015).

DOI: 10.1039/C4RP00125G.

K. Orwat, P. Bernard, A. Migdal-Mikuli, Alternative conceptions of common salt hydrolysis among upper-secondary-school students. J. Balt. Sci. Educ., 16 (1), 64–76 (2017).

P. C. Hardiyanti, W. Sumarni, C, Kurniawan, The Effect of Application of Problem Based Learning Model Learn-ing on Salt Hydrolysis Material Learning Outcomes (2018).

http://www.ijere.com/frontend//uploads/submissionfolder/prahasti-hardiyanti/the-effect-of-application-of-problem-based-learning-model-learning-on-salt-hydrolysis-material-learning-outcomes-m0YPP.docx (date of access 06.07.2019.)

C. K. Chu, K. Y. Hong, Misconceptions in the teaching of chemistry in secondary school in Singapore & Malay-sia. In Innovative Thoughts, Invigorating Teaching: Pro-ceedings of the Sunway Academic Conference, Swan Convention Centre, Bandar Sunway, 7 August 2009, pp. 1–10 (2010).

H. Elham, K. A. Dilmaghani, Students’ Misconceptions on Acid-Base Chemistry. Basic Education College Mag-azine For educational and Humanities Sciences, 43, 743–753 (2019).

S. Supatmi, A. Setiawan, Y. Rahmawati, Students’ mis-conceptions of acid-base titration assessments using a two-tier multiple-choice diagnostic test. Afr. J. Chem. Educ., 9(1), 18–37 (2019).

N. Seçken, Identifying Student’s Misconceptions about SALT. Procedia Soc. Behav. Sci., 2, 234–245 (2010). DOI:10.1016/j.sbspro.2010.03.004

A. Cokelez, A Comparative Study of French and Turkish Students' Ideas on Acid-Base Reactions. J. Chem. Educ., 87(1), 102-106 (2010). DOI: 10.1021/ed800017b

N. Seçken, E. U. Alşan, The effect of constructivist ap-proach on students’ understanding of the concepts related to hydrolysis. Procedia Soc. Behav. Sci., 15, 235–240 (2011).

DOI: 10.1016/j.sbspro.2011.03.079

J. J. Gongden, E. J. Gongden, Y.N. Lohdip. Assessment of the Difficult Areas of The Senior Secondary School 2 (Two) Chemistry Syllabus of The Nigeria Science Cur-riculum, Afr. J. Chem. Educ., 1(1), 48–61 (2011).

J. van Merriënboer, P. Ayres, Research on Cognitive Load Theory and Its Design Implications for E-Learning. Educ. Technol. Res. Dev., 53(3), 5–13 (2005). DOI: 10.1007/BF02504793

G. Markansky, T. S. Terkildsen, R. E., Mayer, Role of subjective and objective measures of cognitive processing during learning in explaining the spatial contiguity effect. Learn. Instr., 61, 23–34 (2019).

DOI: 10.1016/j.learninstruc.2018.12.001

P. A. Kirchner, F., Kirchner, Mental Effort in Encyclope-dia of the Sciences of Learning, N. M. Seel (Ed.), Spring-er, 2012, pp 2182–2184.

DOI:10.1007/springerreference_226189

M. C. Kernan, N. S. Bruning, L. Miller-Guhde, Individu-al and Group Performance: Effects of Task Complexity and Information. Human performance, 7(4), 273–289 (1994). DOI: 10.1207/s15327043hup0704_3

R. J. Nadolski, P. A. Kirschner, J. J. Merrienboer, J. Woretshofer, Development of an Instrument for Measur-ing the Complexity of Learning Tasks. Educ. Res. Eval., 11(1), 1–27 (2005).

DOI: 10.1080/13803610500110125

G. Sun, S. Yao, J. A. Carretero, An Investigation of the Relation between the Complexity of problem Structure and Mental Effort. Proceedings of the Human Factors and Ergonomics Society - 57th Annual Meeting, Califor-nia, USA - United States, 30.09.2013. (2013).

DOI: 10.1177/1541931213571057

G. S. Halford, W. H. Wilson, S. Phillips, Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behav. Brain. Sci., 21, 803–865 (1998).

DOI: 10.1017/S0140525X98001769

R. C. Daniel, S. E. Embertson, Designing Cognitive Complexity in Mathematical Problem-Solving. Applied Psychological Measurement, Appl. Psychol. Meas., 34(5), 348–364 (2010).

DOI: 10.1177/0146621609349801

S. E. Embertson, R. C. Daniel, Understanding and quanti-fying cognitive complexity level in mathematical problem solving items. Psychol. Sci. Q., 50(3), 328–334, (2008).

D. Batra, Cognitive complexity in data modeling: Causes and recommendations. Requir. Eng., 12(4), 231–244 (2007). DOI: 10.1007/s00766-006-0040-y

H. Hsu, E. A. Silver, Cognitive Complexity of Mathemat-ics Instructional Task in a Taiwanese Classroom: An ex-amination of Task Sources. J. Res. Math. Educ., 45(4), 460–496 (2014).

DOI: 10.5951/jresematheduc.45.4.0460

D. C. Maynard, M. D. Hakel, Effect of Objective and Subjective Task Complexity on Performance. Hum. Per-form., 10(4), 303–330 (1997).

DOI: 10.1207/s15327043hup1004_1.

R. A. Campbell, Task Complexity: A Review and Analy-sis. ‎Acad. Manag. Rev., 13(1), 40–52 (1988). DOI: 10.5465/AMR.1988.4306775

K. Knaus, K. Murphy, A. Blecking, A., T. Holme, A Valid and Reliable Instrument for Cognitive Complexity Rating Assignment of Chemistry Exam Items. J. Chem. Educ., 88(5), 554–560 (2011). DOI: 10.1021/ed900070y

J. R. Raker, J. M. Trate, T. A. Holme, K. Murphy, Adap-tation of an Instrument for Measuring the Cognitive Complexity of Organic Chemistry Exam Items. J. Chem. Educ., 90(10), 1290–1295 (2013).

DOI: 10.1021/ed400373c

N. Pippenger, Complexity theory. Sci. Am., 238(6), 114–125 (1978)

O. Goldreich, Computational Complexity: A Conceptual Perspective. Cambridge University Press, 2008.

J. Sweller, Cognitive load during problem solving: Ef-fects on learning. Cogn. Sci., 12(2), 257–285 (1988). DOI: 10.1207/s15516709cog1202_4

J. Sweller, P. Ayres, S. Kalyuga, Cognitive Load Theory. Springer (2011)

S. Kalyuga, Managing Cognitive Load in Adaptive Mul-timedia Learning. Information Science reference. (2009).

M. Segedinac, M. Segedinac, Z. Konjović, G. Savić, A formal approach to organization of educational objectives, Psihologija, 44(4) 307–323, (2011).

DOI: 10.2298/PSI1104307S .

K. S. Taber, The Use of Cronbach’s Alpha When Devel-oping and Reporting Research Instruments in Science Education. Res. Sci. Educ., 48, 1273–1296 (2018).

DOI: 10.1007/s11165-016-9602-2

R. Ebel, D. Frisbie, Essentials of Educational Measure-ment. Prentice Hall, 1991

S. Moss, H. Prosser, H. Costello, N. Simpson, P. Patel, S. Rowe, S., Tuner, C. Hatton, Reliability and validity of the PAS-ADD checklist for detecting psychiatric disor-ders in adults with intellectual disability. J. Intellect. Dis-abil. Res., 42(2), 173–183.

DOI: 10.1046/j.1365-2788.1998.00116.x

K. M. Loewenthal, C. A. Lewis, An introduction to psy-chological tests and scales (2 ed). Psychology Press (2001). DOI: 10.4324/9781315782980

M. Tavakol, R. Dennick, Making sense of Cronbach’s alpha. Int. J. Med. Educ., 2, 53–55. (2011).

DOI: 10.5116/ijme.4dfb.8dfd

A. Jonsson, G. Svingby, The use of scoring rubrics: Reliability, validity and educational consequences. Educ. Res. Rev., 2(2), 130–144 (2002).

DOI: 10.1016/j.edurev.2007.05.002

S. S. Pande, R. P. Pande, V. P. Parate, A. N. Nikam, S. H. Agrekar, Correlation between difficulty and dis-crimination indices of MCQs in formative exam in physi-ology, SE. Asian J. Med. Educ., 7(1), 45–50 (2013).

M. H. Towns, Guide to developing high-quality, reliable, and valid multiple-choice assessments. J. Chem. Educ., 91(9), 1426−1431 (2014).

DOI: 10.1021/ed500076x

A. M. Zubairi, N. L. Abu Kassim, Classical and rasch analyses of dichotomously scored reading comprehension test items. Mal. J. ELT Res., 2, 1-20 (2006)

A. Mayers, Introduction to Statistics and SPSS in Psy-chology. Pearson Education Limited (2013)

S. A. Horvat, M. D. Segedinac, D. D. Milenković, T. N. Hrin, Development of procedure for the assessment of cognitive complexity of stoichiometric tasks. Maced. J. Chem. Chem. Eng., 35(2), 275–284 (2016).

DOI: 10.20450/mjcce.2016.893

S. A. Horvat, D. D. Rodić, M. D. Segedinac, T. N. Rončević, (2017). Evaluation of Cognitive Complexity of Tasks for the Topic Hydrogen Exponentin the Solu-tions of Acids and Bases. J. Sub. Did., 2(1), 33–45 (2017). DOI: 10.5281/zenodo.1238972

S. A. Horvat, T. N. Rončević, D. Z Arsenović., D. D. Rodić, M. D. Segedinac, Validation of the procedure for the assessment of cognitive complexity of chemical tech-nology problem tasks. J. Balt. Sci. Educ., 19(1), 64–75 (2020). DOI: 10.33225/jbse/20.19.64

J. D. Evans, Straightforward statistics for the behavioral sciences. Thomson Brooks/Cole Publishing Co (1996).

J. Cohen, Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates (1988).

N. Brace, R. Kemp, R. Snelgar, SPSS for Psychologists: A guide to data analysis using SPSS for Windows (3rd ed), Routledge (2006).

J. Liu, D. J. Harris, A. Schmidt, (2006). 33 Statistical Procedures Used in College Admissions Testing. Handbk. Stat., 26, 1057–1091 (2006).

DOI: 10.1016/S0169-7161(06)26033-4

K. Schweizer, C. DiStefano, Principles and Methods of Test Construction, Hogrefe Publishing (2016).

Y. K.Yan, R. Subramaniam, Using a multi-tier diagnostic test to explore the nature of students’ alternative concep-tions on reaction kinetics. Chem. Educ. Res. Pract., 19, 213–226. (2018).

DOI: 10.1039/C7RP00143F

G. Demircioglu, Comparison of the effects of conceptual change texts implemented after and before instruction on secondary school students' understanding of acid-base concepts. Asia-Pacific Forum Sci. Learn. Teach., 10 (2), 1–29, (2009).

N. Kala, F. Yaman, A. Ayas, The Effectiveness of Pre-dict–Observe–Explain Technique In Probing Students’ Understanding about Acid–Base Chemistry: A Case for the Concepts of pH, pOH, and Strength. Int. J. Sci. Math. Educ., 11, 555—574 (2013).

DOI: 10.1007/s10763-012-9354-z

P. J., Garnett, P. Garnett, M. W. Hackling, Stu-dents’alternative conceptions in chemistry: A review of research and implications for teaching and learning. Stud, Sci. Educ., 25(1), 69–96, (1995).

DOI: 10.1080/03057269508560050

S. Olić, J. Adamov J. The relationship between learning styles and students’ chemistry achievement. Maced. J. Chem. Chem. Eng., 37(1), 79–88, (2018).

DOI: 10.20450/mjcce.2018.1400

Y. R. Putri, Analysing of students' misconceptions on salt hydrolysis chemistry at senior high school in Pa-dangsidempuan, (Tesis). Universitas Medan, (2014).




DOI: http://dx.doi.org/10.20450/mjcce.2021.2240

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 Saša Antal Horvat, Jovana Mihajlović, Tamara Rončević, Dušica Rodic

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.