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To efficiently create books and other instructional content from videos and further improve accessibility of our course content we needed to solve the scene detection (SD) problem for engineering educational content. We present the pedagogical applications of extracting video images for the purposes of digital book generation and other shareable resources, within the themes of accessibility, inclusive education, universal design for learning and how we solved this problem for engineering education lecture videos. Scene detection refers to the process of merging visually similar frames into a single video segment, and subsequent extraction of semantic features from the video segment (e.g., title, words, transcription segment and representative image). In our approach, local features were extracted from inter-frame similarity comparisons using multiple metrics. These include numerical measures based on optical character recognition (OCR) and pixel similarity with and without face and body position masking. We analyze and discuss the trade-offs in accuracy, performance and computational resources required. By applying these features to a corpus of labeled videos, a support vector machine determined an optimal parametric decision surface to model if adjacent frames were semantically and visually similar or not. The algorithm design, data flow, and system accuracy and performance are presented. We evaluated our system using videos from multiple engineering disciplines where the content was comprised of different presentation styles including traditional paper handouts, Microsoft PowerPoint slides, and digital ink annotations. For each educational video, a comprehensive digital-book composed of lecture clips, slideshow text, and audio transcription content can be generated based on our new scene detection algorithm. Our new scene detection approach was adopted by ClassTranscribe, an inclusive video platform that follows Universal Design for Learning principles. We report on the subsequent experiences and feedback from students who reviewed the generated digital-books as a learning component. We highlight remaining challenges and describe how instructors can use this technology in their own courses. The main contributions of this work are: Identifying why automated scene detection of engineering lecture videos is challenging; Creation of a scene-labeled corpus of videos representative of multiple undergraduate engineering disciplines and lecture styles suitable for training and testing; Description of a set of image metrics and support vector machine-based classification approach; Evaluation of the accuracy, recall and precision of our algorithm; Use of an algorithmic optimization to obviate GPU resources; Student commentary on the digital book interface created from videos using our SD algorithm; Publishing of a labeled corpus of video content to encourage additional research in this area; and an independent open-source scene extraction tool that can be used pedagogically by the ASEE community e.g., to remix and create fun shareable instructional content memes, and to create accessible audio and text descriptions for students who are blind or have low vision. Text extracted from each scene can also used to improve the accuracy of captions and transcripts, improving accessibility for students who are hard of hearing or deaf.

Noticing children's mathematical thinking is foundational to teaching that is responsive to children's thinking. To better understand the range of noticing expertise for teachers engaged in multiyear professional development, we assessed the noticing of 72 upper elementary school teachers using three instructional scenarios involving fraction problem solving. Through a latent class analysis, we identified three subgroups of teachers that reflected different profiles of noticing expertise. Consideration was given to the noticing component skills of attending to children's strategy details, interpreting children's understandings, and deciding how to respond on the basis of children's understandings. We share theoretical and practical implications for not only the three profiles but also our choice to explore separately two versions of deciding how to respond (deciding on follow-up questions and deciding on next problems). [For the complete proceedings, see ED630060.]

Isomorphism and homomorphism are topics central to abstract algebra, but research on mathematicians' views of these topics, especially with respect to sameness, remains limited. This study examines 197 mathematicians' views of how sameness could be helpful or harmful when studying isomorphism and homomorphism. Instructors saw benefits to connecting isomorphism and sameness but expressed reservations about homomorphism. Pedagogical considerations and the dual function-structure nature of isomorphism and homomorphism are also explored. [For the complete proceedings, see ED630060.]

This study aims to describe how preservice secondary mathematics teachers (PSMTs) reason about different function representations. The study focuses on two PSMTs' reasonings across static and dynamic representations of functions. Sfard's (2008) Theory of Commognition guided our analysis. Findings indicate that while static representations restrict attention given to covariation, dynamic representations support PSMTs' reasoning about covariation including making connections to how covariation is represented in static graphs. [For the complete proceedings, see ED630060.]

People often view mathematics as abstract, cold, and irrelevant to real-life, and their school experiences influence such views. In this case study, we investigated the mathematics learning experiences of two women who participated in an afterschool girls STEM club 26 years ago. We explored their experiences in and out of school and how such experiences informed their images of mathematics. Data were collected from a survey, focus group interviews, and individual interviews. Using qualitative analysis, we learned that their school mathematics experiences influenced the participants' images of mathematics. The findings also revealed the participants' continuous and discontinuous learning experiences between school and out-of-school mathematics. This study suggests creating spaces to develop curricula that bridge the gap between school and out-of-school learning experiences. [For the complete proceedings, see ED630060.]

This article describes the results of an investigation based on a Models and Modeling Perspective [MMP]. We present the evolution of the models built by university students when solving a model development sequence designed to promote their learning of the exponential function. As a result, we observed that students' thinking was modified, expanded, and refined, as they developed different iterations of their models. Students' models evolved by creating, first, linear models that required direction; second, models where there was no dissociation between linear and exponential behavior; then, situated exponential models; and finally, sharable, and reusable exponential models. [For the complete proceedings, see ED630060.]

The demand for U.S. temporary workers has doubled since the 1990s, especially after the technology boom. American employers have benefited from hiring foreign talent for STEM occupations. Despite the mandatory prevailing wage regulations, temporary skilled immigrants have been criticized for their "willingness" to work for lower wages. This integrated literature review aims to clarify the wage gap between skilled immigrants and natives in STEM occupations. This research design utilized a systematic literature review to identify relevant studies, collect data, and analyze data using thematic coding. Findings included two major themes: the wage gap as a "myth" and the wage gap as a "reality." Practical and policy implementations will be discussed based on the findings of this integrated literature review. [For the complete volume, "American Association for Adult and Continuing Education Inaugural 2020 Conference Proceedings (Online, October 27-30, 2020)," see ED611534.]

Examining students' inclinations to use algorithms and rules to solve a task was a fruitful area of research in chemical education in the last four decades. This research aimed to examine whether students read the task request carefully, considering its meaningfulness, or they approach it mechanically, applying a set of algorithms by default. The research sample consisted of students majoring in chemistry teaching at the University of Novi Sad, Faculty of Sciences who were in their final year of bachelor studies. The study was conducted during two academic years. The main instrument consisted of five quantitative problems, and each of the problems contained deceptive information that made the calculation nonsensical. The results revealed that most students applied an algorithmic approach without paying attention to the meaningfulness of the task requirements. Additionally, it has been shown that students rely heavily on memorizing formulas without a proper understanding of underlying concepts. [For the full proceedings, see ED620289.]

Mathematical calculations are an important part of chemistry. Those problems are difficult for students, especially if the task is set with a limiting reactant. The aim of this study was development of a Procedure for evaluation of cognitive complexity of the Stoichiometric Tasks with a Limiting Reactant. The procedure created included an assessment of the difficulty of concepts and an assessment of their interactivity. As a research instrument for assessing performance, the test of knowledge was specifically constructed for this research. Each task in the test was followed by a seven-point Likert scale for the evaluation of the invested mental effort. The research included 58 upper-secondary students. The validity of the procedure was confirmed by a series of regression analyses where statistically significant correlation coefficients are obtained among the examined variables: students' achievement and invested mental effort from cognitive complexity (independent variable). [For the full proceedings, see ED620289.]

These proceedings contain papers of the 4th International Baltic Symposium on Science and Technology Education (BalticSTE2021) held in Šiauliai, Lithuania, June 21-22, 2021. This symposium was organized by the Scientific Methodical Center "Scientia Educologica" in cooperation with Scientia Socialis, Ltd. Lithuania. The proceedings are comprised of 16 short papers. Keynote speakers include: Paul Pace, Paolo Bussotti, Peter Demkanin, and Malgorzata Nodzynska. [Individual papers are indexed in ERIC.]

This study examines pre-service teachers' (PSTs') views of tasks that engage students in mathematical argumentation. Data were collected in two different mathematics courses for elementary school education majors (n = 51 total PSTs). Analyzed were (a) written journals in which PSTs defined tasks that promote student engagement in argumentation, (b) tasks PSTs posed to engage students in mathematical argumentation, and (c) accompanying explanations in which PSTs motivated tasks they posed. The analysis revealed that PSTs interpret tasks that foster argumentation in terms of activities of argumentation that a task elicits and space for argumentation that the task provides. Several features that PSTs associated with each of the two major task characteristics were identified. While posing tasks to engage students in argumentation, PSTs did not place equal emphasis on all of the identified features. [For the complete proceedings, see ED629884.]

In this study, we investigated two students', ages ten and eleven, emotions while they engaged in mathematical problem solving. During three task-based interviews, the students explored parts of the unsolved problem the Graceful Tree Conjecture. While they were engaged in the interviews, they self-identified the emotions of frustration and joy they were feeling using the Wong-Baker Scale. The students displayed the interplay of the emotions of frustration and joy or which we consider to be productive struggle. A descripted case of Georgia is included to describe her emotions while problem solving. [For the complete proceedings, see ED629884.]

This report proposes a framework for describing student analogical reasoning activities in abstract algebra that moves beyond the traditional literature-based treatment of analogical mapping. The Analogical Reasoning in Mathematics (ARM) framework captures the activities that students engage in when anticipating, creating, and reasoning from mathematical analogies. This considers activities along several dimensions including: inter/intra domain activity, foregrounded/backgrounded domain, and attention to similarity/difference. These dimensions are integrated with Gentner's (1983) analogical mapping framework to characterize student activity when they are presented with tasks where reasoning by analogy can productively support their mathematical investigations. By characterizing these activities, we can better develop tasks to support students in productively analogizing between mathematical domains. [For the complete proceedings, see ED629884.]

We report on findings from two one-on-one teaching experiments with prospective middle school teachers (PTs). The focus of each teaching experiment was on identifying and explicating the mental processes and types of intermediate, supporting reasoning that each PT used in their development of combinatorial reasoning. The teaching experiments were designed and facilitated to guide each PT toward reinventing multiple combinatorial formulas. Drawing on a subset of this data, we describe the development of the PTs' mental processes and reasoning as they came to construct formulas for counting permutations and arrangements without repetition, and we analyze our findings through a psychological constructivist framework. [For the complete proceedings, see ED629884.]

One important aspect of teaching is reviewing tasks in preparation for instruction. The goal of the multicase study of four secondary teachers was to examine the interplay between their mathematical knowledge for teaching [MKT] and what they attend to when reviewing a mathematics task. We engaged secondary mathematics teachers in a semi-structured, clinical interview focused on a nonroutine mathematical task involving exponential growth. The results suggest experienced teachers may not explicitly attend to learning opportunities in their review of a task, and their own mathematical work contributes to their anticipation of student work and thinking. This work highlights how researchers focused on MKT can use clinical interviews as a tool for extracting and describing a teacher's MKT. [For the complete proceedings, see ED629884.]

We report our findings and perspective to document the knowledge exhibited by three experienced high school teachers in their instructional decisions for lessons on the equation of an exponential function. We describe the nature of the mathematical ideas and connections teachers promoted in discourse and the decisions that supported the emergence and connections of the mathematics. Despite similarities in the structure of the mathematical activities, differences existed in the ideas that emerged in the three teachers' discussions regarding the relationship between the exponent value and the independent variable. We describe links between collections of teacher decisions to their influences on the mathematics discourse. [For the complete proceedings, see ED629884.]

Using neurodiversity as our theoretical framework, rather than a deficit or medical model, we analyze the narratives of five dyslexic research mathematicians to find common strengths and challenges for dyslexic thinkers at the highest level of mathematics. We report on 3 themes: (1) highly visual and intuitive ways of mathematical thinking; (2) pronounced issues with memorization of mathematical facts and procedures; and (3) resilience as a strength of dyslexia that matters in mathematics. We introduce the idea of Neurodiversity for Mathematics, a research agenda to better understand the strengths (as well as challenges) of neurodiverse individuals and to use that knowledge to design better mathematical learning experiences for all. [For the complete proceedings, see ED629884.]

Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students' performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine learning models, there is increasing evidence and concerns about bias and unfairness of these models. Unfair models can lead to inequitable outcomes for some groups of students and negatively impact their learning. We show by real-world examples that educational data has embedded bias that leads to biased student modeling, which urges the development of fairness formalizations and fair algorithms for educational applications. Several formalizations of fairness have been proposed that can be classified into two types: (i) group fairness and (ii) individual fairness. Group fairness guarantees that groups are treated fairly as a whole, which might not be fair to some individuals. Thus individual fairness has been proposed to make sure fairness is achieved on individual level. In this work, we focus on developing an individually fair model for identifying students at-risk of underperforming. We propose a model which is based on the idea that the prediction for a student (identifying at-risk students) should not be influenced by his/her sensitive attributes. The proposed model is shown to effectively remove bias from these predictions and hence, making them useful in aiding all students. [For the full proceedings, see ED607784.]

Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of policies that benefit all students. We explore these issues in the context of using multi-armed bandit (MAB) algorithms to learn a policy for what version of an educational technology to present to each student, varying the relation between student characteristics and outcomes and also whether the algorithm is aware of these characteristics. Through simulations, we demonstrate that the inclusion of student characteristics for personalization can be beneficial when those characteristics are needed to learn the optimal action. In other scenarios, this inclusion decreases performance and increases variation in student experiences. Moreover, including unneeded student characteristics can systematically disadvantage students with less common values for these characteristics. Our simulations do however suggest that real-time personalization will be helpful in particular real-world scenarios, and we illustrate this through case studies using existing experimental results in ASSISTments. Overall, our simulations show that adaptive personalization in educational technologies can be a double-edged sword: real-time adaptation improves student experiences in some contexts, but the slower adaptation and increased variability mean that a more personalized model is not always beneficial. [For the full proceedings, see ED607784.]

Selecting courses in an open-curriculum education program is a difficult task for students and academic advisors. Course recommendation systems nowadays can be used to reduce the complexity of this task. To control the recommendation error, we argue that course recommendations need to be provided together with "statistical" confidence. The latter can be used for computing a statistically valid set of recommended courses that contains courses a student is likely to take with a probability of at least 1-[epsilon] for a user-specified significance level [epilsilon]. For that purpose, we introduce a generic algorithm for course recommendation based on the conformal prediction framework. The algorithm is used for implementing two conformal course recommender systems. Through experimentation, we show that these systems accurately suggest courses to students while maintaining statistically valid sets of courses recommended. [For the full proceedings, see ED607784.]

Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation. [For the full proceedings, see ED607784.]