Impact of Digitalized Predict-Observe-Explain Model on Achievement in Genetics among Secondary School Students in Oshimili South Local Government Area, Delta State
Year: 2024
Type: article
Abstract: The study investigated the impact of digitalized Predict-Observe-Explain Model on achievement in genetics among secondary school students in Oshimili South Local Government Area of Delta State. The study employed a non-randomized non-equivalent pre-test post-test quasi-experimental design. The population of the study was all the SS class 2 biology students in the fourteen public schools in the local government area. Multi-stage sampling technique was adopted to select two coeducational schools for the study. Only coeducational schools with at least 30 biology students were selected for the study. This is to enable formation of at least ten groups of three students per group in the experimental group. The experimental group (N=31; Male =14; female =17) was taught genetics using the digital POE (D-POE) model while the control group (N = 38; male = 20; female = 18) were taught the same concepts using the traditional method for a period of six weeks of 80 minutes per week. A 30-item two-tier Diagnostic multiple-choice Test of Achievement in Genetics (TAG) adapted and modified from West African Examinations Council’s past question papers in Biology from 2009 to 2019 was used to collect data. The reliability coefficient of the instrument was found to be 0.75 by Kudder Richardson 21. Analysis of covariance showed that the mean scores of the groups differed significantly (F=1, 68) = 287.395, P=.000), thus hypothesis one was retained. Also, the t-test showed a no significant difference in mean scores due to sex (t(1.41; df =29; P≤ .168). Hypothesis two was rejected. This result showed that adopting the D-POE model provides the impetus for students to overcome achievement challenges in genetics across in both sexes. Further studies with larger samples for longer periods is recommended. Additional factors, such as student preferences, prior technology experience, instructional variations within the model, or teacher characteristics should be investigated to enhance generalization of the findings. Keyword: Predict-Observe-Explain, Genetics, Technology-enhanced teaching, Achievement.
Author: Moemeke, D.U
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6949616203
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