Development of a construct map to describe students’ reasoning about introductory quantum mechanics

Uccio, U. Scotti di; Colantonio, Arturo; Galano, Silvia; Marzoli, Irene; Trani, F.; Testa, Italo

doi:10.1103/physrevphyseducres.16.010144

Cited by 11 publications

(20 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The transformative context was constituted by a teaching-learning sequence (TLS) on the same introductory QM contents and with the same duration (about 14 h) [47]. The TLS followed a conceptual sequence that starts from the energy exchange between matter and electromagnetic radiation, addresses the Heisenberg's principle and atom stability, and arrives to atomic energy levels, orbitals, and the energy band model.…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

“…Through the proposed activities, the students build increasingly sophisticated models of energy exchanges between radiation and matter, build a connection between energy discretization and atomic stability, and finally exploit more complex models to explain the behavior of metals and insulators. The pedagogical approach of the transformative TLS was a guided inquiry approach [48][49][50][51][52][53][54] that included also potential pedagogical countermeasures for overconfidence [47] as the critical and the regulatory feedback strategy; and the "think the opposite" strategy. In such a way, students were not only expected to develop a sounder scientific knowledge about QM, but also a greater metacognitive awareness about-and confidence in-what they were learning.…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

“…Students' increasing knowledge about the topics targeted in the traditional and transformative teaching sequences was modeled by the construct map validated and revised in Ref. [47]. We summarize the TLS activities in Table I.…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

“…The complete questionnaire is reported in Ref. [47]. To measure confidence, we appended to each item a second tier, asking respondents to what extent they felt confident in the given answer on a scale from 1 (not at all confident) to 5 (completely confident).…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

“…Accuracy and confidence data came from the same sample involved in Ref. [47]. The TLS group included N ¼ 200 students, while the textbook group involved N ¼ 208 students.…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

See 4 more Smart Citations

Effects of instruction on students’ overconfidence in introductory quantum mechanics

Testa¹,

Colantonio

Galano³

et al. 2020

Phys. Rev. Phys. Educ. Res.

Self Cite

View full text Add to dashboard Cite

Students' ability to assess their own knowledge is an important skill in science education. However, students often overestimate their actual performances. In such cases, overconfidence bias arises. Previous studies in physics education have shown that overconfidence bias concerns mainly content areas, such as Newtonian mechanics, where misconceptions are strongly held by students. However, how the received instruction and the levels of understanding of a given topic influence overconfidence bias is yet to be proved. In this paper, we address this issue choosing as content area introductory quantum mechanics (QM). Overall, 408 high school students were involved in the study and randomly assigned to two experimental groups. One group received a textbook-based instruction about introductory QM, whereas the other one received instruction on the same topics through an innovative guided inquiry teaching-learning sequence (TLS), which included also potential pedagogical countermeasures for overconfidence bias. Students of both experimental groups completed a multiple-choice questionnaire and indicated for each item the degree of their confidence in the given answer using a 5-point Likert scale. The overconfidence bias was quantitatively defined and evaluated at person level using a 1D Rasch model. Progress in knowledge about the targeted topics was modeled according to a construct map validated in a previous paper. Results show that, for the whole sample, the overconfidence bias decreased as students progressed along the levels of the construct map. However, findings indicate that students of the TLS group achieved a significantly higher accuracy and a better confidence calibration, while the textbook group exhibited a lower performance and a significantly greater overconfidence bias. Implications for research into overconfidence bias in physics education are briefly discussed.

show abstract

Section: A Instructional Context Of the Studymentioning

confidence: 99%

Section: A Instructional Context Of the Studymentioning

confidence: 99%

Section: A Instructional Context Of the Studymentioning

confidence: 99%

Section: A Instructional Context Of the Studymentioning

confidence: 99%

“…Accuracy and confidence data came from the same sample involved in Ref. [47]. The TLS group included N ¼ 200 students, while the textbook group involved N ¼ 208 students.…”

Section: A Instructional Context Of the Studymentioning

confidence: 99%

See 3 more Smart Citations

Effects of instruction on students’ overconfidence in introductory quantum mechanics

Testa¹,

Colantonio

Galano³

et al. 2020

Phys. Rev. Phys. Educ. Res.

Self Cite

View full text Add to dashboard Cite

show abstract

Rasch Measurement in Discipline-Based Physics Education Research

Ding

2023

Contemporary Trends and Issues in Science Education

View full text Add to dashboard Cite

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

et al. 2022

View full text Add to dashboard Cite

Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.

show abstract

Development of a construct map to describe students’ reasoning about introductory quantum mechanics

Cited by 11 publications

References 59 publications

Effects of instruction on students’ overconfidence in introductory quantum mechanics

Effects of instruction on students’ overconfidence in introductory quantum mechanics

Rasch Measurement in Discipline-Based Physics Education Research

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

Contact Info

Product

Resources

About