<p>The process of scientific inquiry is critical for students to understand how knowledge is developed and validated. Representations of the process of inquiry have varied over time, from simple to complex, but some concepts are persistent – such as the concept of a scientific hypothesis. Current guidelines for undergraduate biology education prioritize developing student competence in generating and evaluating hypotheses but fail to define the concept and role of hypotheses. The nature of science literature points to the hypothetico-deductive method of inquiry originated by Karl Popper as a widely accepted conception of scientific hypotheses. Popper characterized a hypothesis as a falsifiable explanation of observed phenomena deduced from previously established knowledge. Alongside hypotheses, Popper also emphasizes the role of predictions, which are logically derived from hypotheses and characterized as testable expectations regarding the outcomes of an experiment or study. Together, hypotheses and predictions are thought to provide a framework for establishing rigorous conclusions in scientific studies. However, the absence of explicit definitions of hypotheses, or predictions, in guidelines and assessment for biology higher education makes it difficult to determine the current relevance of this perspective on hypotheses and predictions in teaching and learning. This leaves us with an unanswered question – what do biology undergraduate students need to know about scientific hypotheses? We addressed this question over three studies each investigating conceptions of scientific hypotheses, and the related concept of predictions, in a different context – (a) contemporary biology research communications, (b) a case study of biology faculty, graduate teaching assistants, and undergraduate students at a single institution, and (c) a national survey of biology faculty members. We found that the terms “hypothesis” and “prediction” used in varied ways in biology research communication and, most notably, often not connected with each other. We also found variation in conceptions of both hypothesis and prediction among faculty members, both in our case study and in the national survey. Our results indicate that faculty members did not always distinguish between the terms hypothesis and prediction in research or teaching or approach them the same way in research contexts. However, they had largely consistent ideas of the underlying reasoning connecting these concepts to each other and to scientific inquiry. Among graduate teaching assistants and undergraduate students in the case study, we found variation in conceptions of both hypotheses and predictions that was different from conceptions held by faculty members. Both graduate teaching assistants and undergraduate students often did not connect the two concepts in terms of underlying reasoning. Overall, our results indicate that there are some misalignments between students’ and instructors’ conceptions of hypotheses and predictions and their role in inquiry. We further discuss these findings in the context of teaching implications for undergraduate biology.</p>

BACKGROUND: Telepresence robots provide real-time audio, video, and mobility features, allowing faculty and students to engage in learning experiences without being physically present. PROBLEM: With multiple students and faculty members needing to quarantine due to the COVID-19 pandemic, a flexible learning environment was essential. APPROACH: The telepresence robots were used as an innovative approach for both faculty and students to engage in learning experiences offered in a variety of settings. OUTCOME: Feedback was obtained from faculty and students about the use of and satisfaction with telepresence robots. The robots were easy to use and posed only a few technological challenges, which were easily overcome. CONCLUSIONS: Telepresence robots were effective tools in overcoming teaching and learning barriers caused by the COVID-19 pandemic. The telepresence robots have many applications, including use in clinical and community settings.

This thesis focuses on a year-long project of implementing active learning in undergraduate dynamics courses at Cal Poly San Luis Obispo from 2012-2013. The purpose is to increase conceptual understanding of critical dynamics concepts and to repair misconceptions of the students. Conceptual understanding in Dynamics is vital to understanding the big picture, building upon previous knowledge, and better understanding the behavior of engineering systems. Through various hands-on activities, students make predictions, test their conceptions, and solve real world problems. These active learning methods allow students to improve their learning of Dynamics concepts. Education research on active learning is present in Physics and Mathematics disciplines, yet is still growing in Engineering. Four Inquiry-Based Learning Activities (IBLAs) and two Model-Eliciting Activities (MEAs) are discussed in this thesis. Inquiry-Based Learning Activities feature student prediction and experimentation in which the physical world acts as the authority. On the other hand, Model-Eliciting-Activities prompt students to solve real world problems and deliver results to a client. From the results, some activities yield an increase in conceptual understanding, as measured by assessment items, while others do not yield a significant increase. These activities not only help to promote conceptual gains, but also to motivate students and offer realistic engineering contexts. In conclusion, the six total IBLA and MEAS will continue in practice and be improved in their implementation. This thesis work will contribute to engineering education research of active learning methods, and improve the undergraduate dynamics curriculum locally at Cal Poly.

<p>  </p> <p>Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding—has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or instruction, especially if those inferences influence pedagogical decisions or student outcomes. ML offers an opportunity to better personalizing learners’ experiences using those inferences, even within large engineering classrooms. However, neglecting to verify the trustworthiness of ML-derived inferences can have wide-ranging negative impacts on the lives of learners. </p> <p><br></p> <p>This study investigated what student clusters exist within the standard operational data of a large first-year engineering course (>1500 students). This course focuses on computational thinking skills for engineering design. The clustering data set included approximately 500,000 assessment data points using a consistent five-scale criterion-based grading framework. Two clustering techniques—N-TARP profiling and K-means clustering—examined criterion-based assessment data and identified student cluster sets. N-TARP profiling is an expansion of the N-TARP binary clustering method. N-TARP is well suited to this course’s assessment data because of the large and potentially high-dimensional nature of the data set. K-means clustering is one of the oldest and most widely used clustering methods in educational research, making it a good candidate for comparison. After finding clusters, their interpretability and trustworthiness were determined. The following research questions provided the structure for this study: RQ1 – What student clusters do N-TARP profiling and K-means clustering identify when applied to structured assessment data from a large engineering course? RQ2 – What are the characteristics of an average student in each cluster? and How well does the average student in each cluster represent the students of that cluster? And RQ3 – What are the strengths and limitations of using N-TARP and K-means clustering techniques with large, highly structured engineering course assessment data?</p> <p><br></p> <p>Although both K-means clustering and N-TARP profiling did identify potential student clusters, the clusters of neither method were verifiable or replicable. Such dubious results suggest that a better interpretation is that all student performance data from this course exist in a single homogeneous cluster. This study further demonstrated the utility and precision of N-TARP’s warning that the clustering results within this educational data set were not trustworthy (by using its W value). Providing this warning is rare among the thousands of available clustering methods; most clustering methods (including K-means) will return clusters regardless. When a clustering algorithm identifies false clusters that lack meaningful separation or differences, incorrect or harmful educational inferences can result. </p>

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This study compared three instructional approaches-- computer assisted instruction, cooperative learning, and teacher directed instruction--to determine their relative effectiveness in improving math performance of low achieving students. Additional information was collected on student time on task behavior to determine the relative impact of these treatments on this variable. An experimental research design was used. Ninety-nine rising sixth grade students were randomly assigned to one of the three instructional delivery groups for a five week summer remediation program. Classroom teachers self selected the treatment approach they used based on interest and personal experience. Additional training in the use of these strategies was provided prior to the beginning of summer school. Fourth grade students' scores on the math subtest Iowa Test of Basic Skills (ITBS) served as the baseline data for assigning students to one of the treatment groups. A subsequent ITBS math score was obtained on the same students as fifth graders with the latter score serving as the pretest measure. At the end of the summer program the ITBS math subtest was readministered to students to obtain posttest dependent measures on math concepts, math problems, math computations and math total. These data were analyzed with an ANCOVA with the fifth grade ITBS math total score serving as the covariate. While substantial academic growth was reported for all groups on the math total measure, it was found that no significant difference existed between the three groups on improving student performance on math concepts, math problems, math computations, or math total. On the time on task measure, students’ off task behavior observed was minimal and differences reported were not found to be significant. / Ed. D.

<p dir="ltr">The study investigates the effectiveness of productive failure learning using a contrasting-cases design of ill-structured problems followed by well-structured problems. Fifty-one future elementary school teachers, enrolled in an undergraduate physics course were randomly assigned to one of the three conditions: a) ill-structured followed by well-structured problems (IS-WS), b) well-structured followed by well-structured problems (WS-WS), and c) ill-structured followed by ill-structured problems (IS-IS). The study hypothesized that the first condition with a contrasting-case design would outperform the non-contrasting-case design. After solving treatment problems in their respective conditions, all the participants took a post-test that comprised both ill-structured and well-structured problems. The one-way and two-way ANOVA results showed that while productive failure learning (IS-WS) outperformed WS-WS on both procedural and conceptual knowledge in the well-structured post-test, there was no significant difference between the three learning conditions in the ill-structured post-test. The findings indicated that structuring instruction lies on a continuum between highly structured and unstructured. For higher-level physics education, productive failure learning provided the optimum balance of discovery learning via ill-structured problems and guided instruction via well-structured problems to activate prior knowledge, draw attention to critical features of the canonical concept, and facilitate motivation and excitement within learners, resulting in effective learning.</p>