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Asia-Pacific Forum on Science Learning and Teaching, Volume 17, Issue 2, Article 9 (Dec., 2016) |
Research Design and Study Samples
The study was based on a 2x2 (time x group) design. Participants’ science learning outcomes (nanotechnology content knowledge, science process skills, scientific attitudes), and creative thinking were measured both before and after interventions. The participants were two classes of Grade 11 students in one secondary school in the northeast of Thailand, simple random sampling from 10 classes, which were re-sampled into one experimental group (n=49) and one control group (n=34).The intervention was applied in nanotechnology: a Grade 11 supplementary science course.
The constructionism and neurocognitive-based teaching model developed by the researchers was used to teach the experimental group, and the conventional teaching model was used for the control group. The syntax of constructionism and the neurocognitive-based teaching model consists of six steps as follows:
(i)Boost attention: Teacher prepares students to be ready for the new lesson. Teacher stimulates students’ learning interest through their presentation. Teacher makes the students interested in receiving data, motivated to learn, and stimulated their brain. Together, teacher and students shared and defined individual learning and workloads;
(ii)Gather information: Teacher practices students’ divergent thinking and abilities to search information via information technology. Teacher provides opportunities for students to seek knowledge through new sources of learning. Teachers have to prepare materials such as study materials, computer programs or a real object;
(iii)Understanding: Teacher helps students to construct their own knowledge. Students have to review or rethink their assignment. Students find out the relationship between seeking information and constructing their knowledge;
(iv)Thoughts organized: Teacher insists students construct their own knowledge by organizing their ideas. Students are encouraged to share, analyse and debate their projects and find out more information;
(v)Idea clarification/looking for something new: Teacher continues to encourage students to construct their own knowledge by brainstorming, sorting, and making connections between prior knowledge and new knowledge. Teacher promotes divergent thinking, imagination and creation of something new;
(iv)Idea tested: Teacher performs a test or proof of the new invention. Students review the objectives and carefully consider their work.
Finally, students compared significant positive and negative effects and made a presentation (refer to Appendix A and B).
Similarly, the conventional teaching model was used to teach the control group. The syntax of the conventional teaching model is comprised of three steps, namely: introduction, instruction and conclusion. This conventional teaching model is a teacher centred approach and is very common in education. The conventional teaching model disregards the students consequently the mental level of interest of the students. It involves coverage of the context and rote memorization on the part of the students. It did not involve students in creative thinking and participation in the creative part of activities. Most of the time, during the teaching and learning process, instruction remains lateral, which is considered to be accepted activity (Khalid &Azeem, 2012).
Pre-test and post-test was measured on science learning outcomes, namely: nanotechnology content knowledge, science process skills, scientific attitudes, as well as creative thinking before and after intervention. Two sets of lesson plans were developed, one for the experimental group and the other for the control group. Each set of lesson plans consisted of nine lesson plans for two hours per week, giving a total of 24 hours. There were eight subtopics and the time allocated for each subtopic was: (i) basic knowledge of nanotechnology (two hours); (ii) nanotechnology in nature (two hours); (iii) activated carbon with nanotechnology (two hours); (iv) nano products inventing tools (two hours); (v) how to invent nano products (eight hours); (vi) uses of nanotechnology (two hours); (vii) understanding nanotechnology (two hours), and (viii) nanotechnology products (four hours).
Research instruments were mainly used as tests to measure science learning outcomes and creative thinking. A total of four types of instrument were utilized in this study (refer to Appendix C). The Nanotechnology Content Knowledge Test was used to measure the science learning outcome of nanotechnology content knowledge. It comprised 30 multiple choice items that were selected from the school item bank. The reliability (KR20) was 0.87; the discrimination index was 0.21 to 0.64, and the difficulty index was 0.21 to 0.85.
The Science Process Skills Test was used to measure the science learning outcome of science process skills, which consisted of 45 multiple choice items. This instrument was adopted from Bunterm, Lee, Ng, Srikoon, Vangpoomyai, Rattanavongsa and Rachahoon (2014). Thirteen different science process skills were assessed by this Science Process Skills Test, namely: observing, measuring, using numbers and calculating, classifying, space/space relationship and space/time relationship, communication, inferring, predicting, controlling variable, formulating hypotheses, defining operationally, experimenting, and interpreting data and conclusion. The reliability (KR20) was 0.88; the discrimination index was 0.21 to 0.72, and the difficulty index was 0.22 to 0.93.
The Scientific Attitudes Rating Scale was adopted from Bunterm et al. (2014). It was used to measure the science learning outcome of scientific attitudes. This 25-item task rating scale was designed to evaluate the six traits of scientific attitudes, namely: curiosity, reasonableness, responsibility and perseverance, organization and carefulness, honesty, and open-mindedness. The reliability value (α) of this Scientific Attitudes Rating Scale was 0.83.
The Torrance type scientific creative test created by Wongpratum (2000) was adapted and its characteristics were re-examined before it was utilized to measure the creative thinking of students. Three categories of creative thinking were considered, namely: fluency, flexibility, and originality. The total score of creative thinking was cumulated from these three categories. The reliability (Hoyt’s analysis of variance) value originally reported was 0.792. The re-examined reliability was 0.772.
Researchers took one to two hours per week to collect data for the four instruments, before and after the intervention as pre-tests and post-tests, respectively. Firstly, the participants were given a duration of 50 minutes to attend the creative test. This was followed by a 40 minute Nanotechnology Content Knowledge Test, and a 10 minute Scientific Attitudes Rating Scale. Finally, a 50 minute Science Process Skills Test was conducted. All of these tests were conducted on different days within a week.
Repeated measures of multivariate analysis of variance (Repeated MANOVA) were used to analyse the effect of time, teaching model, and interaction between time and teaching model on the four dependent variables: nanotechnology content knowledge, science process skills, scientific attitudes, and creative thinking. Wilks’ lambda, a direct measure of the proportion of variance in the combination of dependent variables that is unaccounted for the group variable (Everitt & Dunn, 1991), was used to test whether there were differences between the means of identified groups of subjects on a combination of dependent variables. However, if some violated assumptions are found, such as if the covariance matrices of the dependent variables were not equal across the group (the Box’s M was significant), this implies a Type I error should be considered, the Pillai's trace, which is the most robust (Olson,1976) will be used instead. Or if the Levene's Test of Equality of Error Variances showed the difference of error variance across groups, the nonparametric test will be used instead for that variable. Furthermore, if some variables are not equal before treatment, researchers will compare the after treatment dependent variables using MANCOVA with the unequal pre-test variables as the covariates.
The adjustment for the pre-test score in MANCOVA has produced two benefits. The first one is to make sure that any post-test differences truly result from the treatment of the teaching model, and are not some left-over effect of (usually random) pre-test differences between the groups. The other benefit is to account for variation around the post-test means that comes from the variation in where the students started at pre-test.
