Development and validation of laboratory assessment tools for science educators

Paper Details

Research Paper 17/05/2026
Views (188)
current_issue_feature_image
publication_file

Development and validation of laboratory assessment tools for science educators

Cinder Dianne L. Tabiolo*
Int. J. Biosci. 28(5), 137-149, May 2026.
Copyright Statement: Copyright 2026; The Author(s).
License: CC BY-NC 4.0

Abstract

The study aimed to develop and validate a tool for evaluating and enhancing the practices and challenges faced by chemistry educators in laboratory instruction, grounded in Piaget’s Theory of Constructivism. The instrument assesses cognitive, affective, and psychomotor domains through rigorous item generation, expert validation, and pilot testing with chemistry and science teachers. It comprises two main components: teaching practices and teaching challenges, categorized into five factors. The appropriateness of factor analysis was confirmed by Keiser–Meyer Olkin (KMO) measures of sampling adequacy (.652 for Parts II and IV, .863 for Part III) and Bartlett’s test (df = 1035, p < .000 for Parts II and IV; df = 300, p < .000 for Part III). Items with factor loadings over 0.40 were retained. The Content Validity Ratio (CVR) was 1.000, and the overall Content Validity Index (CVI) was 1.000 for seven experts. The percentage of variance was 56.80% for Parts II and IV (three factors) and 64.16% for Part III (two factors). Eigenvalues for the five factors ranged from 1.172 to 21.135. Reliability analysis demonstrated strong internal consistency, with an overall Cronbach’s alpha of 0.93 and subscale alphas from 0.87 to 0.96. The identified factors include Instructional Design and Delivery, Classroom Laboratory Management, Assessment and Feedback, Supervision and Safety Management, and Teaching Resources and Support. This instrument provides a valuable framework for optimizing chemistry laboratory instruction.

Abrahams I, Reiss MJ, Sharpe RM. 2013. The assessment of practical work in school science. Studies in Science Education 49(2), 209-251.

Bennett J, Holman J. 2002. Context-based approaches to the teaching of chemistry: What are they and what are their effects? In: Gilbert JK, editor. Chemical Education: Towards Research-based Practice. Dordrecht: Kluwer Academic Publishers. p. 165-184.

Burke KA, Greenbowe TJ, Hand BM. 2006. Implementing the Science Writing Heuristic in the chemistry laboratory. Journal of Chemical Education 83(7), 1032.

DeVellis RF. 2016. Scale Development: Theory and Applications. 4th ed. Thousand Oaks, CA: Sage Publications.

Field A. 2018. Discovering Statistics Using IBM SPSS Statistics. 5th ed. London: Sage Publications.

Hofstein A, Lunetta VN. 2004. The laboratory in science education: Foundations for the twenty-first century. Science Education 88(1), 28–54.

Kaiser HF. 1974. An index of factorial simplicity. Psychometrika 39(1), 31–36.

Kline P. 1994. An Easy Guide to Factor Analysis. London: Routledge.

Kline RB. 2016. Principles and Practice of Structural Equation Modeling. 4th ed. New York: Guilford Press.

Lawshe CH. 1975. A quantitative approach to content validity. Personnel Psychology 28(4), 563–575.

McCrae RR, Terracciano A, Members of the Personality Profiles of Cultures Project. 2005. Universal features of personality traits from the observer’s perspective: Data from 50 cultures. Journal of Personality and Social Psychology 88(3), 547–561.

Muijs D, Reynolds D. 2011. Effective Teaching: Evidence and Practice. 3rd ed. London: Sage Publications.

Stains M, Harshman J, Barker MK, Chasteen SV, Cole R, DeChenne-Peters SE. 2018.  Anatomy of STEM teaching in North American universities. Science 359(6383), 1468-1470.

Van Driel JH, Verloop N. 1999. Teachers’ knowledge of models and modelling in science. International Journal of Science Education 21(11), 1141-1153.

Watkins MW. 2018. Exploratory factor analysis: A guide to best practice. Journal of Black Psychology 44(3), 219–246.

Related Articles

Effects of crop succession on bird communities in an urban wetland in Korhogo (Côte d’Ivoire)

Konan Ekoun Michaël*, Niamien Coffi Jean Magloire, Guétondé Valérie Florence, Yéo N’gan-Nan, Yaokokoré-Béibro Kouassi Hilaire, Int. J. Biosci. 29(3), 94-107, September 2026.

Assessment of physicochemical parameters and bacteriological quality of pond water of Sylhet agricultural university, Sylhet

Parsha Shanzana*, Muktarul Jannat Rafa, Rubayet Jahan Nuha, Int. J. Biosci. 29(3), 87-93, September 2026.

Anther development and microsporogenesis in Swietenia macrophylla King. (Meliaceae): A histological study

Mahantesh Shitaram Chavhan, Shekappa Ningappa Agadi*, Int. J. Biosci. 29(3), 71-86, September 2026.

Intra-population genetic diversity modeling of date palm (Phoenix dactylifera L.) population in Niger

Adamou Ibrahim Maman Laouali*, Zango Oumarou, Alio Moussa Abdourazak, Rafiou Abdoulaye, Idi Saidou Sani, Idi Garba Nana Mariama, Bakasso Yacoubou, Int. J. Biosci. 29(3), 62-70, September 2026.

Biocontrol potential of indigenous Trichoderma isolates against Fusarium wilt of cotton in Tanzania: In vitro screening and screen-house validation

Fauzia Khalid Mpiganzila*, Alfonce Leonard, Lidia Munuo, Ernest Mbega, Agatha Aloyce, Int. J. Biosci. 29(3), 44-61, September 2026.

In vitro study of antioxidant, anti-inflammatory and antihyperglycemic activity of medicinal plants

T. K. Nishath*, P. Mathavi, V. Ambikapathy, P. Prakash, A. Panneerselvam, Int. J. Biosci. 29(3), 32-43, September 2026.

Association between the IL13 rs1800925 (-1112C/T) polymorphism and Schistosoma mansoni infection intensity in a population from western Côte d’Ivoire

Bernardin Ahouty Ahouty*, Abla Edwige Sokouri, Georges Bohoussou Kassi, Innocent Allépo Abé, Martial Kassi N’djetchi, Yaya Ouangbo Ouattara, Ornella Karmelle Lydia Dago, Mathurin Yao Koffi, Thomas Konan Konan, Mathurin N’Goran Koffi, Int. J. Biosci. 29(3), 20-31, September 2026.