Investigating the impact of automated feedback on students’ scientific argumentation

Zhu, Muhua; Lee, Hee‐Sun; Wang, Ting; Liu, Ou Lydia; Belur, Vinetha; Pallant, Amy

doi:10.1080/09500693.2017.1347303

Cited by 50 publications

(32 citation statements)

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“…However, in a recent study, Brenner et al () found that in some cases, low prior knowledge students profited from guidance, but in other cases, they did not. A recent meta‐analysis by Belland et al () indicated that students performing below and above average profited equally from computer‐based scaffolding (of ill‐structured problem‐based curricula in STEM), whereas in other studies, more recent and focusing on inquiry learning, it was found that the higher prior knowledge students used the guidance most and profited most from it (Roll et al, ; van Dijk, Eysink, & de Jong, ; van Riesen et al, ; Zhu et al, ). The explanation for these findings is that a certain level of knowledge is necessary to be able to use the guidance (Roll et al, ; van Riesen et al, ).…”

Section: Effectiveness Of (Technology‐based) Inquiry Learningmentioning

confidence: 99%

“…Kardan and Conati (), for example, diagnosed differences in behaviour between low and high learners in an interactive simulation, designed prompts for learners to stimulate them to change their way of learning if necessary, and found that this positively influenced their learning results. Zhu et al () used c‐rater‐ML as a basis in providing students with automatic feedback on open‐ended written scientific explanations they gave for their choices in online multiple‐choice quizzes related to a virtual laboratory on climate change. These authors found that the great majority of students improved their argumentation after receiving the feedback.…”

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Pmentioning

confidence: 99%

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Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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What is the best approach to educating students is, evidently, the pivotal question in educational research. In the general debate on this question, clear positions are often taken―for example, whether teacher‐led instruction or more student‐directed approaches should be followed. In the current narrative review, this central question and the different stances taken are explored for a specific field of learning, STEM (science, technology, engineering, and mathematics) domains. The current article starts with an argument as to why the traditional, more directive teacher‐led educational approach to STEM learning may not always lead to deep conceptual knowledge and proposes exploring more engaged forms of learning. More specifically, a particular arrangement for engaged learning, inquiry learning, is taken as an example in this exploration. It is argued that the effects of educational methods are influenced by a multitude of (interacting) factors related to student and teacher characteristics, context, domain, frequency of use, timing, and probably more. Therefore, this article tries to outline a more nuanced and balanced stance towards choosing educational approaches. A picture is presented to show that adopting and combining different approaches, at both the lesson and the curricular levels, may be necessary to reach optimal levels of learning. Existing and upcoming technologies may play a decisive role in realizing this more complex, but also more realistic view of learning in STEM.

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Section: Effectiveness Of (Technology‐based) Inquiry Learningmentioning

confidence: 99%

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Pmentioning

confidence: 99%

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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“…These studies were post hoc analyses of student responses to develop automated scoring models. Though it is not yet common place (Martin & Sherin, ), researchers have begun exploiting the instructional potential of automated text scoring in real‐time during science instruction in explanation (Linn et al, ) as well as in argumentation (Zhu et al, ). Linn et al () found that students who received automated feedback in real‐time on their explanations performed as well as those who received delayed teacher feedback, indicating that automated feedback can play an important scaffolding tool in the classroom.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated text scoring and real‐time adjustable feedback: Supporting revision of scientific arguments involving uncertainty

et al. 2019

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This paper describes HASbot, an automated text scoring and real‐time feedback system designed to support student revision of scientific arguments. Students submit open‐ended text responses to explain how their data support claims and how the limitations of their data affect the uncertainty of their explanations. HASbot automatically scores these text responses and returns the scores with feedback to students. Data were collected from 343 middle‐ and high‐school students taught by nine teachers across seven states in the United States. A mixed methods design was applied to investigate (a) how students’ utilization of HASbot impacted their development of uncertainty‐infused scientific arguments; (b) how students used feedback to revise their arguments, and (c) how the current design of HASbot supported or hindered students’ revisions. Paired sample t tests indicate that students made significant gains from pretest to posttest in uncertainty‐infused scientific argumentation, ES = 1.52 SD, p < 0.001. Linear regression analysis results indicate that students' HASbot use significantly contributed to their posttest performance on uncertainty‐infused scientific argumentation while gender, English language learner status, and prior computer experience did not. From the analysis of videos, we identified several affordances and limitations of HASbot.

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“…In einem aktuellen Review zur Forschung zu ML (Zhai et al 2020) wird mit Blick auf nationale Standards (NGSS Lead States 2013) gefordert: "... future studies should explore using ML results to support other scientific practices such as modelling ...", und damit ML über Themengebiete wie naturwissenschaftliches Erklären (Linn et al 2014) und Argumentieren (Zhu et al 2017) auszuweiten. Ein in verschiedenen Studien (z.…”

Section: Introductionunclassified

Maschinelles Lernen mit Aussagen zur Modellkompetenz

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Krell

2020

ZfDN

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Zusammenfassung Verfahren des maschinellen Lernens können dazu beitragen, Aussagen in Aufgaben im offenen Format in großen Stichproben zu analysieren. Am Beispiel von Aussagen von Biologielehrkräften, Biologie-Lehramtsstudierenden und Fachdidaktiker*innen zu den fünf Teilkompetenzen von Modellkompetenz (NTraining = 456; NKlassifikation = 260) wird die Qualität maschinellen Lernens mit vier Algorithmen (naïve Bayes, logistic regression, support vector machines und decision trees) untersucht. Evidenz für die Validität der Interpretation der Kodierungen einzelner Algorithmen liegt mit zufriedenstellender bis guter Übereinstimmung zwischen menschlicher und computerbasierter Kodierung beim Training (345–607 Aussagen je nach Teilkompetenz) vor, bei der Klassifikation (157–260 Aussagen je nach Teilkompetenz) reduziert sich dies auf eine moderate Übereinstimmung. Positive Korrelationen zwischen dem kodierten Niveau und dem externen Kriterium Antwortlänge weisen darauf hin, dass die Kodierung mit naïve Bayes keine gültigen Ergebnisse liefert. Bedeutsame Attribute, die die Algorithmen bei der Klassifikation nutzen, entsprechen relevanten Begriffen der Niveaufestlegungen im zugrunde liegenden Kodierleitfaden. Abschließend wird diskutiert, inwieweit maschinelles Lernen mit den eingesetzten Algorithmen bei Aussagen zur Modellkompetenz die Qualität einer menschlichen Kodierung erreicht und damit für Zweitkodierungen oder in Vermittlungssituationen genutzt werden könnte.

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Investigating the impact of automated feedback on students’ scientific argumentation

Cited by 50 publications

References 34 publications

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Automated text scoring and real‐time adjustable feedback: Supporting revision of scientific arguments involving uncertainty

Maschinelles Lernen mit Aussagen zur Modellkompetenz

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