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RESEARCH AND TEACHING
Comparing Academically Homogeneous and Heterogeneous Groups in an Active Learning Physics Class
Many methods have been developed for managing groups in active learning classes, but little research has been done on the effect of group structure itself. Results are presented for an active learning physics class in which half of the class was placed in academically homogeneous groups while the other half was in heterogeneous groups. Students were given the Conceptual Survey of Electricity and Magnetism as a pretest and posttest, and also filled out surveys on their experiences in their groups. The study was intended to be continued for three years, but was terminated halfway through the second semester as the evidence had become sufficiently compelling that placing half of the class in academically heterogeneous groups was placing them at a significant disadvantage. Student feedback, pretest, and posttest data indicated that low- and middle-performing students benefitted the most from academically homogeneous groups. Results for the one full semester of the study and the rationale for discontinuing are presented.
Many methods have been developed for managing groups in active learning classes, but little research has been done on the effect of group structure itself. Results are presented for an active learning physics class in which half of the class was placed in academically homogeneous groups while the other half was in heterogeneous groups. Students were given the Conceptual Survey of Electricity and Magnetism as a pretest and posttest, and also filled out surveys on their experiences in their groups.
Many methods have been developed for managing groups in active learning classes, but little research has been done on the effect of group structure itself. Results are presented for an active learning physics class in which half of the class was placed in academically homogeneous groups while the other half was in heterogeneous groups. Students were given the Conceptual Survey of Electricity and Magnetism as a pretest and posttest, and also filled out surveys on their experiences in their groups.
RESEARCH AND TEACHING
Concept Maps for Structuring Instruction and as a Potential Assessment Tool in a Large Introductory Science Course
Journal of College Science Teaching—July/August 2020 (Volume 49, Issue 6)
By Carl-Georg Bank and Heidi Daxberger
Concept maps make connections between ideas apparent, and thus would seem ideally suited to demonstrate learning. Yet, they are not widely used by instructors, particularly in large university classes. In this paper we review the strengths and rationale behind concept maps and provide examples we have used to structure content for one of our introductory courses. A rubric that focuses on propositions was used to grade student-created concept maps on a final exam administered to 293 students. These grades are compared to those of multiple-choice questions, short answers, and an essay on the same exam. We find poor correlation between concept maps and the other instruments, and stronger correlations between short answers and multiple choice, as well as between short-answer content grades and essay writing grades. We speculate that concept maps may be less influenced by student language skills and short-term memory than written answers, and that concept maps should be used as an alternative assessment of higher order thinking skills.
Concept maps make connections between ideas apparent, and thus would seem ideally suited to demonstrate learning. Yet, they are not widely used by instructors, particularly in large university classes. In this paper we review the strengths and rationale behind concept maps and provide examples we have used to structure content for one of our introductory courses. A rubric that focuses on propositions was used to grade student-created concept maps on a final exam administered to 293 students.
Concept maps make connections between ideas apparent, and thus would seem ideally suited to demonstrate learning. Yet, they are not widely used by instructors, particularly in large university classes. In this paper we review the strengths and rationale behind concept maps and provide examples we have used to structure content for one of our introductory courses. A rubric that focuses on propositions was used to grade student-created concept maps on a final exam administered to 293 students.
RESEARCH AND TEACHING
Measuring Computational Thinking Teaching Efficacy Beliefs of Preservice Elementary Teachers
Journal of College Science Teaching—July/August 2020 (Volume 49, Issue 6)
By Erdogan Kaya, Anna Newley, Ezgi Yesilyurt, and Hasan Deniz
With the release of the Next Generation Science Standards (NGSS), assessing K–12 science teachers’ self-efficacy in Computational Thinking (CT) is an important research gap to study. Bandura defines self-efficacy as awareness of the individual’s potential and capabilities to accomplish a goal. Teaching efficacy beliefs is a significant identifier of teachers’ performance and motivation in teaching the specific content successfully; however, K–12 science teachers’ CT teaching efficacy beliefs are rarely discussed. Participating preservice elementary teachers (PSET) were enrolled in an undergraduate elementary science teaching methods course during the spring and summer 2018 semesters in a southwestern state university. We administered a CT teaching efficacy beliefs survey at the beginning and end of the related unit (i.e., the intervention). During the intervention, the PSET followed the CT practices by building educational robots, coding visual block-based programs, and solving puzzles in the video game “Zoombinis.” In this paper, we report the impact of the intervention on teaching efficacy beliefs of PSET. We used SPSS software to analyze our quantitative results. We performed paired samples t-test for the two teaching efficacy beliefs subscales, Personal Computational Thinking Teaching Efficacy (PCTTE) and Computational Thinking Teaching Outcome Expectancy (CTTOE), to measure if there is a significant difference in teaching efficacy beliefs. Our research findings suggest that introducing CT increases PSET CT teaching efficacy beliefs. Furthermore, based on the results of our exploratory research with PSET, we propose implications of the study for K–12 CT teaching efficacy beliefs and CT education research.
With the release of the Next Generation Science Standards (NGSS), assessing K–12 science teachers’ self-efficacy in Computational Thinking (CT) is an important research gap to study. Bandura defines self-efficacy as awareness of the individual’s potential and capabilities to accomplish a goal. Teaching efficacy beliefs is a significant identifier of teachers’ performance and motivation in teaching the specific content successfully; however, K–12 science teachers’ CT teaching efficacy beliefs are rarely discussed.
With the release of the Next Generation Science Standards (NGSS), assessing K–12 science teachers’ self-efficacy in Computational Thinking (CT) is an important research gap to study. Bandura defines self-efficacy as awareness of the individual’s potential and capabilities to accomplish a goal. Teaching efficacy beliefs is a significant identifier of teachers’ performance and motivation in teaching the specific content successfully; however, K–12 science teachers’ CT teaching efficacy beliefs are rarely discussed.
RESEARCH AND TEACHING
Negative Student Response to Active Learning in STEM Classrooms:
A Systematic Review of Underlying Reasons
Journal of College Science Teaching—July/August 2020 (Volume 49, Issue 6)
By Prateek Shekhar, Maura Borrego, Matt DeMonbrun, Cynthia Finelli, Caroline Crockett, and Kevin Nguyen
Recent research has supported the use of student-centered teaching practices, such as active learning, because of its effectiveness in improving student learning and retention when compared with traditional, lecture-based teaching practices. Despite evidence supporting the effectiveness of active learning in improving STEM undergraduate education, the adoption of active learning by instructors has been slow for reasons, including negative student response to active learning. In this systematic literature review, we examine students’ negative responses to active learning and reasons for the negative responses noted in 57 published STEM studies. Our findings identify three types of negative responses: affect, engagement, and evaluation. The reasons behind negative response represented six overarching categories based on student feedback: limited value, lack of time, difficulty and increased workload, lack of guidance, logistical difficulties, unfamiliarity with active learning, lack of preparation, and confidence. We leverage different theoretical perspectives to explain the reasons behind negative responses and offer insights for lowering the barrier for instructors to adopt active learning in STEM classrooms.
Recent research has supported the use of student-centered teaching practices, such as active learning, because of its effectiveness in improving student learning and retention when compared with traditional, lecture-based teaching practices. Despite evidence supporting the effectiveness of active learning in improving STEM undergraduate education, the adoption of active learning by instructors has been slow for reasons, including negative student response to active learning.
Recent research has supported the use of student-centered teaching practices, such as active learning, because of its effectiveness in improving student learning and retention when compared with traditional, lecture-based teaching practices. Despite evidence supporting the effectiveness of active learning in improving STEM undergraduate education, the adoption of active learning by instructors has been slow for reasons, including negative student response to active learning.
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Student Perceptions of Open Educational Resources Video-Based Active Learning in University-Level Biology Classes:
A Multi-Class Evaluation
Journal of College Science Teaching—July/August 2020 (Volume 49, Issue 6)
By Gary D. Grossman and Troy N. Simon
We quantified student perceptions of an active learning exercise, based on open-educational video resources, in both a first-year seminar class (Natural Environment of Athens and Georgia, three sections), and a larger lecture class (Natural History of Georgia [FANR 1200, two sections), designed for nonscience majors. Evaluation of STEM pedagogical techniques over multiple classes and levels are uncommon. In general, the frequencies of students in different majors within classes did not differ. In all classes, except FANR 2015, students reported more positive than negative answers in Likertscale questionnaires, although even in the latter class 55% of students responded positively to doing scientific research. Students did not display a preference for different aspects of the exercise (four of five classes), and previous experience in science also did not affect positive responses to the questionnaire. In FANR 1200, year in school had a contradictory effect on positive responses. Triangulation interview trend analyses indicated that a high proportion of students (mean 65%, range 41–89%) stated the exercise was both new and represented deeper learning. These results confirm that active learning exercises may help recruit new students to STEM disciplines.
We quantified student perceptions of an active learning exercise, based on open-educational video resources, in both a first-year seminar class (Natural Environment of Athens and Georgia, three sections), and a larger lecture class (Natural History of Georgia [FANR 1200, two sections), designed for nonscience majors. Evaluation of STEM pedagogical techniques over multiple classes and levels are uncommon. In general, the frequencies of students in different majors within classes did not differ.
We quantified student perceptions of an active learning exercise, based on open-educational video resources, in both a first-year seminar class (Natural Environment of Athens and Georgia, three sections), and a larger lecture class (Natural History of Georgia [FANR 1200, two sections), designed for nonscience majors. Evaluation of STEM pedagogical techniques over multiple classes and levels are uncommon. In general, the frequencies of students in different majors within classes did not differ.
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Qualitative Analysis of Ray Optics in a College Physics Laboratory: A 5E Lesson
This paper describes an alternative approach to teaching and learning practices in an undergraduate physics laboratory. The instructor plans and implements the 5E instructional model into the laboratory instruction. The article includes an example of the 5E lesson for the laboratory component of a physics course, which has separate lecture and laboratory sections. Through the use of a learning model, students could make predictions, exchange ideas, collect and analyze data, and construct evidence-based explanations about the ray optics topic. This approach of teaching in a physics laboratory promoted students’ engagement in scientific practices and learning of the fundamental concepts. The design of the lesson can be used as a model of a physics laboratory instructor’s attempt to put inquirybased instructional strategies into practice through the 5E cycle. This model can be useful for other instructors who are willing to improve or change their teaching practices and students’ learning experiences.