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This paper discusses two "GeoGebra" applets, "Radical" and "Parabola," that are designed to strengthen the conceptual understanding of specific topics in algebra. The design and pedagogical basis of the applets are presented. The integration of the applets in teaching Grade 9 mathematics in a partner high school in the Philippines is then discussed. Finally, we report feedback gathered from students and teachers during this integration. Their feedback indicates the potential of these applets for improving students' learning of algebra. [For the full proceedings, see ED659933.]

The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the generalizability of Knowledge Training (KT) models by comparing model performance in predicting student knowledge within the academic year and across academic years. Models were trained on data from two popular open-source curricula available through Open Educational Resources. We observed that the models generally were highly performant in predicting student learning within an academic year, whereas certain academic years were more generalizable than other academic years. We posit that the Knowledge Tracing models are relatively stable in terms of performance across academic years yet can still be susceptible to systemic changes and underlying learner behavior. As indicated by the evidence in this paper, we posit that learning platforms leveraging KT models need to be mindful of systemic changes or drastic changes in certain user demographics. [For the complete proceedings, see ED630829. Additional funding was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN) program.]

Teachers often rely on the use of a range of open-ended problems to assess students' understanding of mathematical concepts. Beyond traditional conceptions of student open-ended work, commonly in the form of textual short-answer or essay responses, the use of figures, tables, number lines, graphs, and pictographs are other examples of open-ended work common in mathematics. While recent developments in areas of natural language processing and machine learning have led to automated methods to score student open-ended work, these methods have largely been limited to textual answers. Several computer-based learning systems allow students to take pictures of hand-written work and include such images within their answers to open-ended questions. With that, however, there are few-to-no existing solutions that support the auto-scoring of student hand-written or drawn answers to questions. In this work, we build upon an existing method for auto-scoring textual student answers and explore the use of OpenAI/CLIP, a deep learning embedding method designed to represent both images and text, as well as Optical Character Recognition (OCR) to improve model performance. We evaluate the performance of our method on a dataset of student open-responses that contains both text- and image-based responses, and find a reduction of model error in the presence of images when controlling for other answer-level features. [For the complete proceedings, see ED630829.]

Tests of statistical significance often play a decisive role in establishing the empirical warrant of evidence-based research in education. The results from pattern-based assessment items, as introduced in this paper, are categorical and multimodal and do not immediately support the use of measures of central tendency as typically related to interpretations of measures of statistical significance. Responses from the duplicate implementation of selected pattern-based items (PBIs) in successive grades (3-8) as part of the statewide Interim Assessment Program in Texas are used to illustrate how non-parametric methods can be used to establish statistically significant comparisons of student results. Not all the repeat-item results improved across years. [For the complete proceedings, see ED657822.]

This paper discusses the "Just Keep Solving" apps that are designed based on deliberate practice model for developing mathematical skills. Features of deliberate practice include well-defined goals involving areas of weakness as determined by a knowledgeable other such as a teacher. The integration of game design features provides a positive environment wherein the learning goals critical in a deliberate practice model are emphasized. Possible strategies for integrating the apps in a classroom are also discussed using Hughes, Thomas and Scharber's RAT (Replacement, Amplification, Transformation) framework. The games can replace traditional pen-and-paper classroom activities, amplify learning by personalizing a student's experience and providing opportunities for deliberate practice, and transform instruction from being teacher-centered to student-centered. [For the full proceedings, see ED639391.]

This paper discusses the distribution, through a digital datacasting framework, of mathematical resources for Grades 1 to 7 to two schools in a community in the Philippines. Among the mathematical resources made available, are mathematical applications (apps), which run on mobile technologies, that have been created to help in the mathematical learning of students in a remote setting. The distribution is facilitated by the RuralCasting set-top box, a developed custom set-top box capable of receiving digital TV broadcasts and providing local content access through its Wi-Fi network. This paper presents the performance of the set-top box in distributing the mobile mathematical apps, and a short discussion on the mathematical applications deployed. [For the full proceedings, see ED639391.]

The 360 Degree Global Education Model (360 Global Ed Model) provides a comprehensive framework for creating meaningful interdisciplinary student team learning through international service learning. Providing successful multi-disciplined undergraduate education necessitates creating a foundation for productive team-science based learning between disciplines. The pedagogical foundation employed by Breitkreuz and Songer within the 360 Global Ed model creates a shared values student dialogue for enhancing undergraduate team learning and performance. This paper provides a summary of the 360 Global Ed Model, a discussion of methods used to create a student shared dialogue ("Team Learning Foundation"), lessons learned, and student outcomes. Methods for shared dialogues include creating a team-defined mission, team-defined behavioral standards, building shared cultural understandings and expectations for developing cultural intelligence, and developing realistic expectations for interpersonal understandings through use of self-assessments. Student team-led projects provide an authentic context impetus for implementing the team learning foundation. [For the full proceedings, see ED631133.]

It is particularly important to identify and address issues of fairness and equity in educational contexts as academic performance can have large impacts on the types of opportunities that are made available to students. While it is always the hope that educators approach student assessment with these issues in mind, there are a number of factors that likely impact how a teacher approaches the scoring of student work. Particularly in cases where the assessment of student work requires subjective judgment, as in the case of open-ended answers and essays, contextual information such as how the student has performed in the past, general perceptions of the student, and even other external factors such as fatigue may all influence how a teacher approaches assessment. While such factors exist, however, it is not always clear how these may introduce bias, nor is it clear whether such bias poses measurable risks to fairness and equity. In this paper, we examine these factors in the context of the assessment of student answers to open response questions from middle school mathematics learners. We observe how several factors such as context and fatigue correlate with teacher-assigned grades and discuss how learning systems may support fair assessment. [This paper was published in: "Proceedings of the 30th International Conference on Computers in Education," edited by S. Iyer et al., Asia-Pacific Society for Computers in Education, 2022.]