@article{KontroBuschhueter2020, author = {Kontro, Inkeri and Buschh{\"u}ter, David}, title = {Validity of Colorado Learning Attitudes about Science Survey for a high-achieving, Finnish population}, series = {Physical review. Physics education research}, volume = {16}, journal = {Physical review. Physics education research}, number = {2}, publisher = {American Physical Society}, address = {College Park, MD}, issn = {2469-9896}, doi = {10.1103/PhysRevPhysEducRes.16.020104}, pages = {11}, year = {2020}, abstract = {The Colorado Learning Attitudes about Science Survey (CLASS) is an instrument which is widely used in physics education to characterize students' attitudes toward physics and learning physics and compare them with those of experts. While CLASS has been extensively validated for use in the context of higher education institutions in the United States, there has been less information about its use with European students. We have studied the structural, content, and substantive aspects of validity of CLASS by first doing a confirmatory factor analysis of N = 642 sets of student answers from the University of Helsinki, Finland. The students represented a culturally and demographically different subset of university physics students than in previous studies. The confirmatory factor analysis used a 3-factor, 15-item factor structure as a starting point and the resulting factor structure was similar to the original. Just minor modifications were needed for fit parameters to be in the acceptable range. We explored the differences by student interviews and consultation of experts. With the exception of one item, they supported the new 14-item, 3-factor structure. The results show that the interpretations made from CLASS results are mostly transferable, and CLASS remains a useful instrument for a wide variety of populations.}, language = {en} } @article{VogelsangBorowskiBuschhueteretal.2019, author = {Vogelsang, Christoph and Borowski, Andreas and Buschh{\"u}ter, David and Enkrott, Patrick and Kempin, Maren and Kulgemeyer, Christoph and Reinhold, Peter and Riese, Josef and Schecker, Horst and Schr{\"o}der, Jan}, title = {Entwicklung von Professionswissen und Unterrichtsperformanz im Lehramtsstudium Physik}, series = {Zeitschrift f{\"u}r P{\"a}dagogik}, volume = {65}, journal = {Zeitschrift f{\"u}r P{\"a}dagogik}, number = {4}, publisher = {Beltz}, address = {Weinheim}, issn = {0044-3247}, pages = {473 -- 491}, year = {2019}, abstract = {Angehende Physiklehrkr{\"a}fte sollen im Rahmen ihres Studiums fachliches und fachdidaktisches Wissen erwerben, welches die Gestaltung lernf{\"o}rderlichen Unterrichts erm{\"o}glicht. Es ist allerdings empirisch nur wenig gekl{\"a}rt, wie sich dieses Wissen im Laufe des Studiums entwickelt und ob es zur Ausbildung von Handlungsf{\"a}higkeiten beitr{\"a}gt. Um derartige Wirkungsaussagen treffen zu k{\"o}nnen, m{\"u}ssen Instrumente entwickelt werden, die eine valide Testwertinterpretation zulassen. In diesem Beitrag werden auf Basis von im Projekt Profile-P+ entwickelten Instrumenten Validit{\"a}tsanalysen zur l{\"a}ngsschnittlichen Entwicklung des Professionswissens von Physiklehramtsstudierenden im Verlauf des Bachelorstudiums und ihrer F{\"a}higkeiten zur Planung und Reflexion von Physikunterricht sowie zum Erkl{\"a}ren von physikalischen Sachverhalten vor und nach dem Praxissemester dargestellt. Neben Wissenstests kamen standardisierte Performanztests zum Einsatz. Die vorliegenden Ergebnisse sprechen daf{\"u}r, dass die erhobenen Messwerte im Sinne von Wirkungsaussagen interpretiert werden k{\"o}nnen.}, language = {de} } @article{BuschhueterSpodenBorowski2017, author = {Buschh{\"u}ter, David and Spoden, Christian and Borowski, Andreas}, title = {Physics knowledge of first semester physics students in Germany}, series = {International journal of science education}, volume = {39}, journal = {International journal of science education}, number = {9}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {0950-0693}, doi = {10.1080/09500693.2017.1318457}, pages = {1109 -- 1132}, year = {2017}, abstract = {Over the last decades, the percentage of the age group choosing to pursue university studies has increased significantly across the world. At the same time, there are university teachers who believe that the standards have fallen. There is little research on whether students nowadays demonstrate knowledge or abilities similar to that of the preceding cohorts. However, in times of educational expansion, empirical evidence on student test performance is extremely helpful in evaluating how well educational systems cope with the increasing numbers of students. In this study, we compared a sample of 2322 physics freshmen from 2013 with another sample of 2718 physics freshmen from 1978 at universities in Germany with regard to their physics knowledge based on their results in the same entrance test. Previous results on mathematics knowledge and abilities in the same sample of students indicated that there was no severe decline in their average achievement. This paper compares the physics knowledge of the same two samples of students. Contrary to their mathematics results, their physics results showed a substantial decrease in physics knowledge as measured by the test.}, language = {en} } @phdthesis{Buschhueter2017, author = {Buschh{\"u}ter, David}, title = {Anforderungsrelevante mathematik- und physikbezogene Leistungsdispositionen von Physikanf{\"a}ngerinnen und - anf{\"a}ngern}, school = {Universit{\"a}t Potsdam}, year = {2017}, language = {de} } @article{KulgemeyerBorowskiBuschhueteretal.2020, author = {Kulgemeyer, Christoph and Borowski, Andreas and Buschh{\"u}ter, David and Enkrott, Patrick and Kempin, Maren and Reinhold, Peter and Riese, Josef and Schecker, Horst and Schr{\"o}der, Jan and Vogelsang, Christoph}, title = {Professional knowledge affects action-related skills}, series = {Journal of research in science teaching : the official journal of the National Association for Research in Science Teaching}, volume = {57}, journal = {Journal of research in science teaching : the official journal of the National Association for Research in Science Teaching}, number = {10}, publisher = {Wiley}, address = {Hoboken}, issn = {0022-4308}, doi = {10.1002/tea.21632}, pages = {1554 -- 1582}, year = {2020}, abstract = {Professional knowledge is an important source of science teachers' actions in the classroom (e.g., personal professional content knowledge [pedagogical content knowledge, PCK] is the source of enacted PCK in the refined consensus model [RCM] for PCK). However, the evidence for this claim is ambiguous at best. This study applied a cross-lagged panel design to examine the relationship between professional knowledge and actions in one particular instructional situation: explaining physics. Pre- and post a field experience (one semester), 47 preservice physics teachers from four different universities were tested for their content knowledge (CK), PCK, pedagogical knowledge (PK), and action-related skills in explaining physics. The study showed that joint professional knowledge (the weighted sum of CK, PCK, and PK scores) at the beginning of the field experience impacted the development of explaining skills during the field experience (beta = .38**). We interpret this as a particular relationship between professional knowledge and science teachers' action-related skills (enacted PCK): professional knowledge is necessary for the development of explaining skills. That is evidence that personal PCK affects enacted PCK. In addition, field experiences are often supposed to bridge the theory-practice gap by transforming professional knowledge into instructional practice. Our results suggest that for field experiences to be effective, preservice teachers should start with profound professional knowledge.}, language = {en} } @article{WulffBuschhueterWestphaletal.2020, author = {Wulff, Peter and Buschh{\"u}ter, David and Westphal, Andrea and Nowak, Anna and Becker, Lisa and Robalino, Hugo and Stede, Manfred and Borowski, Andreas}, title = {Computer-based classification of preservice physics teachers' written reflections}, series = {Journal of science education and technology}, volume = {30}, journal = {Journal of science education and technology}, number = {1}, publisher = {Springer}, address = {Dordrecht}, issn = {1059-0145}, doi = {10.1007/s10956-020-09865-1}, pages = {1 -- 15}, year = {2020}, abstract = {Reflecting in written form on one's teaching enactments has been considered a facilitator for teachers' professional growth in university-based preservice teacher education. Writing a structured reflection can be facilitated through external feedback. However, researchers noted that feedback in preservice teacher education often relies on holistic, rather than more content-based, analytic feedback because educators oftentimes lack resources (e.g., time) to provide more analytic feedback. To overcome this impediment to feedback for written reflection, advances in computer technology can be of use. Hence, this study sought to utilize techniques of natural language processing and machine learning to train a computer-based classifier that classifies preservice physics teachers' written reflections on their teaching enactments in a German university teacher education program. To do so, a reflection model was adapted to physics education. It was then tested to what extent the computer-based classifier could accurately classify the elements of the reflection model in segments of preservice physics teachers' written reflections. Multinomial logistic regression using word count as a predictor was found to yield acceptable average human-computer agreement (F1-score on held-out test dataset of 0.56) so that it might fuel further development towards an automated feedback tool that supplements existing holistic feedback for written reflections with data-based, analytic feedback.}, language = {en} } @article{WulffBuschhueterWestphaletal.2022, author = {Wulff, Peter and Buschh{\"u}ter, David and Westphal, Andrea and Mientus, Lukas and Nowak, Anna and Borowski, Andreas}, title = {Bridging the gap between qualitative and quantitative assessment in science education research with machine learning}, series = {Journal of science education and technology}, volume = {31}, journal = {Journal of science education and technology}, number = {4}, publisher = {Springer}, address = {Dordrecht}, issn = {1059-0145}, doi = {10.1007/s10956-022-09969-w}, pages = {490 -- 513}, year = {2022}, abstract = {Science education researchers typically face a trade-off between more quantitatively oriented confirmatory testing of hypotheses, or more qualitatively oriented exploration of novel hypotheses. More recently, open-ended, constructed response items were used to combine both approaches and advance assessment of complex science-related skills and competencies. For example, research in assessing science teachers' noticing and attention to classroom events benefitted from more open-ended response formats because teachers can present their own accounts. Then, open-ended responses are typically analyzed with some form of content analysis. However, language is noisy, ambiguous, and unsegmented and thus open-ended, constructed responses are complex to analyze. Uncovering patterns in these responses would benefit from more principled and systematic analysis tools. Consequently, computer-based methods with the help of machine learning and natural language processing were argued to be promising means to enhance assessment of noticing skills with constructed response formats. In particular, pretrained language models recently advanced the study of linguistic phenomena and thus could well advance assessment of complex constructs through constructed response items. This study examines potentials and challenges of a pretrained language model-based clustering approach to assess preservice physics teachers' attention to classroom events as elicited through open-ended written descriptions. It was examined to what extent the clustering approach could identify meaningful patterns in the constructed responses, and in what ways textual organization of the responses could be analyzed with the clusters. Preservice physics teachers (N = 75) were instructed to describe a standardized, video-recorded teaching situation in physics. The clustering approach was used to group related sentences. Results indicate that the pretrained language model-based clustering approach yields well-interpretable, specific, and robust clusters, which could be mapped to physics-specific and more general contents. Furthermore, the clusters facilitate advanced analysis of the textual organization of the constructed responses. Hence, we argue that machine learning and natural language processing provide science education researchers means to combine exploratory capabilities of qualitative research methods with the systematicity of quantitative methods.}, language = {en} }