Samuel Tobler

Last Name

Tobler

First Name

Samuel

ORCID

0000-0002-0661-6055

Organisational unit

01560 - D-BIOL Center for Active Learning / D-BIOL Center for Active Learning

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Publications 1 - 10 of 28

Advancing university biology education with narratives
Tobler, Samuel (2023)
Many fundamental biological concepts, such as molecular randomness, are characterized by their complexity due to their interdisciplinary nature and associated difficulties in understanding. The use of expository texts to convey these concepts further complicates the learning process due to the unfamiliar text structure and abstract presentation of the contents. Conversely, embedding such concepts in a historical background could enhance comprehension, since the way of obtaining information becomes more intuitive, and abstract concepts can be grounded in real-life experiences. However, the effects of such narrative ways of conveying knowledge on learning, especially in university biology education, have been understudied. The present dissertation addresses this research gap by investigating the role of stories in the learning of complex biological concepts. To this end, four studies were conducted in order to examine under which conditions and for which reasons learning can be positively influenced by using narratives as instructional material. In the first study, a standardized test for reliable and valid estimations of the understanding of molecular randomness was developed. Implementation of the test with first-year students also allowed modeling of conceptual change, and application with biology doctoral students revealed the sometimes deep-seated misconceptions in this topic. The second study systematically analyzed the results of empirical studies on a meta-analytic level. The focus was on articles examining the impact of narratives compared to expository texts on performance in science education. The analysis of the 72 experimental comparisons, compiled from 30 independent studies, showed that learning with stories led to higher performance. On the other hand, it was shown that the type of story, the level of education, and the learning environment significantly contributed to how impactful the narrative instruction was. The third study examined the impact of concepts framed in a historical narrative compared to thematically equivalent expository texts on learning success at different levels of comprehension in higher education. Thereby, the narrative focused on the historical aspects of discovering random molecular motion. The results implied that knowledge transfer was enhanced by the narrative but indicated that prior education played a significant role in how narratives impact learning. Students who had taken fewer biology classes in high school particularly benefited from the narratives, whereas students with a more extensive science background often derived greater benefits from the expository text. The positive influence of stories on performance was also reflected in the learning mechanisms studied, such as the more effectively allocated cognitive load or enhanced self-efficacy. Yet, the assessments differed regarding situational interest and cognitive engagement concerning prior education, whereby stories triggered the investigated learning mechanisms to a greater extent for those students with less than those with more prior biology education. The final study examined undergraduate students’ knowledge transfer of molecular randomness as a direct function of prior knowledge. In addition to the two previously investigated experimental conditions (narrative and expository), a third instructional variation was examined in which a historical background was used as preparation for future learning with the expository text. Bayesian modeling approaches demonstrated that prior knowledge played a significant role in knowledge acquisition and that the latter depended on the instructional method. Students with little prior knowledge benefited most when concepts were embedded in a historical context, whereas students with higher prior knowledge tended to profit from the expository text but only when a narrative preceded the latter. Finally, a structural equation model confirmed the predicted influence of different learning mechanisms on learning biological concepts with narratives. In conclusion, the four studies contributed to developing a framework for capturing the understanding of molecular randomness, meta-analytically and empirically investigating the effect of narratives on learning scientific concepts, and exploring the importance of underlying factors and involved learning mechanisms influencing the effectiveness of narratives in education.
Narratives in University Biology Education: Benefits, Challenges, and Perspectivesd
Tobler, Samuel (2023)
Embedding scientific contents in narrated contexts has been conjectured to promote not only students’ understanding of the nature of science but also their comprehension of the underlying concepts. Yet, empirical research aiming to validate this conjecture has led to ambiguous findings but, at the same time, indicated the potential of storified education. In this talk, I will delve into my research on using narratives in university classrooms to enhance students’ understanding of fundamental concepts in biology. Findings from performance data of several hundreds of ETH students from various disciplines and educational backgrounds and meta-analytic evidence not only helped shed more light on narratives in educational settings but also revealed essential factors that influence a narrative’s scholastic effectiveness. Synthesizing these results, I will discuss the benefits of narratives in education along with associated challenges and pitfalls. Furthermore, I’ll outline future research perspectives in this promising field.
Smart Grading: A Generative AI-Based Tool for Knowledge-Grounded Answer Evaluation in Educational Assessments
Tobler, Samuel (2024)
MethodsX
Evaluating text-based answers obtained in educational settings or behavioral studies is time-consuming and resource-intensive. Applying novel artificial intelligence tools such as ChatGPT might support the process. Still, currently available implementations do not allow for automated and case-specific evaluations of large numbers of student answers. To counter this limitation, we developed a flexible software and user-friendly web application that enables researchers and educators to use cutting-edge artificial intelligence technologies by providing an interface that combines large language models with options to specify questions of interest, sample solutions, and evaluation instructions for automated answer scoring. We validated the method in an empirical study and found the software with expert ratings to have high reliability. Hence, the present software constitutes a valuable tool to facilitate and enhance text-based answer evaluation. • Generative AI-enhanced software for customizable, case-specific, and automized grading of large amounts of text-based answers • Open-source software and web application for direct implementation and adaptation
Towards Personalized Education in Life Sciences: Tailoring Instruction to Students’ Prior Knowledge and Interest Through Machine Learning
Tobler, Samuel; Köhler, Katja (2025)
Trends in Higher Education
Undergraduate life science education faces high attrition rates, especially among students from underrepresented groups. These disparities are often linked to differences in prior knowledge, self-efficacy, and interest, which are rarely addressed in traditional lecture-based instruction. This work explores the use of machine learning-based Intelligent Tutoring Systems (ITSs) to support personalized instruction in biology education by examining stochasticity in molecular systems. Accordingly, we developed and validated a Random Forest classification model and used it to assign instructional materials based on students’ prior knowledge and interests. We then applied the model in an introductory biology classroom and individually estimated the most promising instructional format. Results show that the most effective instruction can be reliably predicted from student performance and interest profiles, and model-based assignments may help reduce pre-existing opportunity gaps. Thus, machine-learning-driven instruction holds promise for enhancing equity in life science education by aligning materials with students’ needs, potentially reducing differences in achievement, self-efficacy, and cognitive load, which might be relevant to promoting underrepresented students. To facilitate a straightforward implementation for educators facing similar challenges associated with teaching molecular stochasticity, we developed an open-access ITS tool and provided a scalable approach for developing similar personalized learning tools.
Narratives and their effects on students' understanding of biology
Tobler, Samuel; Sinha, Tanmay; Köhler, Katja; et al. (2022)
Designing for Problem-Solving prior to Instruction in University Classrooms to Enhance Student Engagement
Tobler, Samuel; Kapur, Manu (2024)
Designing the lecture contents according to the latest trends in the learning sciences, we systematically strived to preface any instruction with a problem-solving phase in which the students were involved in historically foundational or contemporarily relevant research topics circling how (academically) failing cognitively and affectively impacts learning. Thus, combining the nature of research with scientific literature through relevant scholarly articles built the foundation of the course's theoretical aspects. Yet, instead of directly presenting the results, the student's active participation shaped the core of the classes. The students had to discuss findings, generate hypotheses, participate in in-class experiments, and plan their studies. Overall, this resulted in a large portion of time allocated to constructive debates and students interacting with their colleagues with only passive support from the lecturer. To achieve this, we developed three main modes for implementing diverse problem- solving opportunities.
Optimizing Peer Feedback in Higher Education: Fostering Cooperative Responsibility
Tobler, Samuel (2024)
This talk emphasizes enhancing peer feedback through cooperative responsibility in small group discussions and project work, ultimately promoting knowledge transfer and engagement.
Impact of Narrative versus Expository Instruction in Science Education on Recall, Understanding, and Transfer: A Meta-Analysis
Tobler, Samuel; Sinha, Tanmay; Köhler, Katja; et al. (2024)
EdArXiv
Instructional materials in science education from primary school to graduate level are typically written as expository texts. Instead, narratives are only rarely chosen to teach scientific concepts, likely due to the inconclusive evidence regarding the effectiveness of narrative interventions. This meta-analysis synthesizes the results of 30 empirical studies with 72 independent effect sizes representing the academic performance of over 5300 students when learning with either narrative or expository materials. The narrative learning materials had significant and positive effect on performance (g = 0.16; 95% CI: 0.03-0.30, p < .05) but indicated high between-study heterogeneity (I2 = 79%; 95% CI: 75-83%). Follow-up moderator analyses revealed that learning from personal or scientist-centered stories was more favorable than learning from fictional stories. The findings were robust across different education levels. Furthermore, the results indicated that narratives were especially favorable in supporting understanding and more impactful in formal than informal educational settings.
Bridging cognitive neuroscience and education: Insights from EEG recording during mathematical proof evaluation
Gashaj, Venera; Trninic, Dragan; Formaz, Cléa; et al. (2024)
Trends in Neuroscience and Education
Background: Much of modern mathematics education prioritizes symbolic formalism even at the expense of non-symbolic intuition, we contextualize our study in the ongoing debates on the balance between symbolic and non-symbolic reasoning. We explore the dissociation of oscillatory dynamics between algebraic (symbolic) and geometric (non-symbolic) processing in advanced mathematical reasoning during a naturalistic design. Method: Employing mobile EEG technology, we investigated students' beta and gamma wave patterns over frontal and parietal regions while they engaged with mathematical demonstrations in symbolic and non-symbolic formats within a tutor-student framework. We used extended, naturalistic stimuli to approximate an authentic educational setting. Conclusion: Our findings reveal nuanced distinctions in neural processing, particularly in terms of gamma waves and activity in parietal regions. Furthermore, no clear overall format preference emerged from the neuroscientific perspective despite students rating symbolic demonstrations higher for understanding and familiarity.
Finding equivalence: a novel tool to investigate the effect size at which two groups are statistically equivalent
Tobler, Samuel (2022)
In the learning sciences and other disciplines, researchers frequently face the challenge of deciding whether it makes sense from a statistical standpoint to combine the data set of two groups as different cohorts for subsequent analyses or not. Further, especially for studies conducted in educational settings and naturalistic environments, small effects and non-significant differences are common (Bakker et al., 2019). Therefore, it is necessary to be able to profoundly judge whether two groups are different or equivalent within a given set of threshold values and statistical assumptions. Whereas a non-significant difference is often interpreted as a reason to combine two data sets or to conclude that the groups of interest perform similarly, the absence of significant differences does not indicate equivalence of the groups (Edelsbrunner & Thurn, 2018). Meanwhile, research advocating equivalence testing has gained more attention over the last years (Lakens et al., 2018; Mehler et al., 2019). However, it remains the researcher’s decision to choose an appropriate effect size of equivalence and how to report this result. Here, I propose a new approach and ready-to-use online application to determine the precise effect size at which two groups of interest are equivalent. Based on the TOSTER package (Lakens, 2017), which aims to inform about whether two groups are statistically equivalent or not, this hereby described and novel tool accurately calculates the effect size at statistical equivalence. In other words, instead of a dichotomous judgment of equivalence based on a priori set default values including the effect size below which we would assume statistical equivalence (as by applying the TOSTER package), the presented tool does not require any effect size threshold values. Instead, it yields the exact effect size estimate at which the two groups of interest are equivalent, allowing the researcher to make a more informed decision about whether the groups should be combined for further analyses or not. Moreover, it enables the researcher to consistently and unbiasedly report their findings, also in relation to priorly reported studies as suggested by Evans and Yuan (2022), based on which they can ground their decision of combining groups or not or interpret their results. The online tool is available at https://samueltobler.shinyapps.io/findingequivalence.

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Samuel Tobler

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