|Ph.D Student||Revzin Leonid|
|Subject||Adaptive and personalized Learning in the High School|
Chemistry Automated Laboratory - A Multi-Case
|Department||Department of Education in Science and Technology||Supervisors||Professor Igor Verner|
|Dr. Nitza Barnea|
|Full Thesis text - in Hebrew|
The goal of this study is to develop a computer-automated environment for laboratory studies of high school chemistry and explore learning through practice in constructing automation devices and using them for chemical inquiry. The research questions derived from these goals are:
1. What are the characteristics of the computerized and automated laboratory learning environment and what are the features of learning through experiencing the study of chemical processes in this environment?
1a. What are the unique characteristics of the learning environment that combines automation devices of titration processes, taking samples, and dilution solutions?
1b. What are the characteristics of learning chemistry in the computerized and automated lab environment?
1c. What are the characteristics of learning of control and computing technologies in the laboratory environment?
2. What are the technology track students’ perceptions about their self-efficacy to learn chemistry and how they change while experiencing the developing and uses laboratory automation devices?
3. What are the scientific track students’ perceptions of the computerized and automated laboratory learning environment?
Two categories of participants of the study were: students from two comprehensive high schools studying advanced chemistry (N = 284), and students from two high schools (vocational and comprehensive) majoring in mechanics technology (N = 37). The students in the former category were high achievers, while in the latter one, were related to the group of youth at risk.
The answer to the research question 1a is based on the inductive analysis and triangulation of observations of performing laboratory experiments in manual and automated modes, on timing of experiments and technician's work, and on the analysis of logbook records, students' lab reports, and reflections. Seven characteristics of the learning environment were identified. Regarding the question 1b, six properties of chemistry learning in the proposed environment were indicated through observation of students' learning behaviors and analysis of learning outcomes documented in lab reports. We answered the question 1c by using the same instruments as for 1b and found four characteristics of learning of control and computing technologies.
To answer the second research question, we followed up after changes in behavior of the technology students during the course and triangulated results of the self-efficacy questionnaires. We found a significant increase in students' self-efficacy to learn chemistry. To answer the third research question, we analyzed perceptions of learners from the two categories using different instruments. All the scientific track students had very positive perceptions of learning in the automated laboratory environment. The majority of the technological track students gradually changed their perceptions from negative and hostile to attractive and friendly.
To summarize the aforesaid, our study shows that automation of laboratories enables to significantly improve the studies of chemistry in high schools and engage new categories of students to these studies.
The theoretical contribution of this research is in expanding the application of the adaptive and personalized learning theory to chemistry education in the automated laboratory environment. The practical contribution of this study is updating the laboratory environment for chemistry education.