Search

There is emerging evidence that professional noticing is embodied. Yet, there is still a need to better under embodied noticing at a fundamental level, especially from the preservice teachers. This study used traditional and holographic video, along with eye-tracking technology, to examine how preservice teachers' physical act of looking interacts with their professional noticing. The findings revealed that many participants focused on less sophisticated forms of mathematical noticing of students' reasoning. Additionally, results from eye-tracking data suggest that the more participants described students' conceptual reasoning, the more likely they were to focus on how recorded students used their hands to engage in the mathematics. [For the complete proceedings, see ED657822.]

Adults with disabilities face challenges during transition points in their lives, including while transitioning into and out of post-secondary education. Both learners and educators share responsibility for understanding strategies that enhance educational inclusion. Creating an inclusive learning environment provides the means for overcoming transition barriers. This paper will identify practical strategies to address disability complications at transition points. Suggested strategies that assist learners with disabilities include actionable items that learners and educators can implement. Learner strategies focus on self-disclosure, accommodation requests, and communication elements. Educator strategies focus on course design, communication, engagement, and support elements. [For the full proceedings, see ED648717.]

Teaching towards big ideas provide opportunities for teachers to think deeply about content and pedagogy in order to support their students to see connections in mathematics. However, teachers may not always activate or mobilise their knowledge in classroom situations. This paper looks into how a teacher, Peter, think about the tasks in his instructional materials he crafted to uncover what he may notice about the affordances of the tasks for teaching proportionality.

The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may be at risk. The bot's ability to quickly create high quality papers, sometimes complete with reference material, has led to concern that these programs will make students too reliant on their ability and not develop the critical thinking skills necessary to succeed. The rise in these applications has led to the need for the development of detection programs that are able to read the students submitted work and return an accurate estimation of if the paper is human or computer created. These detection programs use natural language processing's (NLP) ideas of perplexity, or randomness of the text, and burstiness, or the tendency for certain words and phrases to appear together, plus sophisticated algorithms to compare the essays to preexisting literature to generate an accurate estimation on the likely author of the paper. The use of these systems has been found to be highly effective in reducing plagiarism among students, however concerns have been raised about the limitations of these systems. False positives, false negatives, and cross language identification are three areas of concern amongst faculty and have led to reduced usage of the detection engines. Despite the limitations however, these systems are a valuable tool for educational institutions to maintain academic integrity and ensure that students are submitting original work. [For the full proceedings, see ED656038.]

As an important part of education, the library should accommodate Inclusive Education to ensure quality and equity. This paper examines ten (10) research abstracts and presentations presented at the Virtual International Conference on Library and Information Science (VICLIS) initiated by the SLTC Research University Sri Lanka, under the subtheme "Library Services for People with Special Needs" to explore the tendency of Library & Information science researchers to adapting the inclusive education in libraries. The sample included 25 papers submitted by LIS researchers from 08 countries to VICLIS conferences and after a review process, 10 papers selected from India, Indonesia, Nigeria, and Sri Lanka were analyzed using the thematic analysis method. Findings indicated that themes such as accessibility to books, improving reading and writing skills in visually impaired students, challenges faced by differently-abled users, the role of libraries in supporting students with disabilities, repackaging of information individual needs, and availability of assistive technology devices in university libraries were dominant in LIS research. Findings imply that a new theme “Inclusive Library Service” should be incorporated into the library service. LIS researchers have recommended providing Braille books, addressing access to information issues, promoting accessibility in library buildings, implementing the Marrakesh Treaty to support visually impaired students, improving staff awareness and training, and collaborating with relevant organizations. Libraries can play a vital role in empowering users with special needs. Future LIS research should concentrate on assistive technologies, information literacy for differently-abled people, collection development, and professional development needs in libraries to enhance inclusive education.

A critical issue in using fine-grained gameplay data to measure learning processes is the development of indicators and the algorithms used to derive such indicators. Successful development--that is, developing traceable, interpretable, and sensitive-to-learning indicators--requires understanding the underlying theory, how the theory is instantiated under different game conditions, and programming that considers these exigencies using fine-grained gameplay data. This study examines preliminary validity evidence on various game-based indicators (GBIs) developed from theoretical frameworks and not from unsupervised feature extraction methods. Examples of indicators are presented that represent three types of indicators: Common indicators, distance-based indicators, and games-specific indicators. For each example, the theoretical background is presented briefly, followed by a description of how the indicator design flows from the theory. Correlational analyses show how the GBIs relate to external criterion measures. Limitations and next steps are discussed.

Students often have limited opportunities in school to grapple with the ramifications of the increasing influence technology companies exert over their lives. Digital citizenship curricula could be the exception, but they typically emphasize personal safety and respectful behavior online, ignoring more participatory or justice-oriented notions of citizenship. The scholarship on critical pedagogy helps to envision a different definition of digital citizenship that challenges the status quo to achieve social justice. Critical digital citizenship curricula are therefore a means for educators and students to use technology and interrogate it in order to effect systemic change. This brief paper examines initial results from the Young People's Race, Power, and Technology Project (YPRPT), an out-of-school program that integrates technology "under the hood" investigations with social justice topics and documentary filmmaking. Youth-produced documentaries on topics such as artificial intelligence in healthcare, the problems as well as the possibilities of automation, and facial recognition technology demonstrate how the young people are developing critical digital citizenship through exploring knotty questions of civics and technology with questions and ideas that seek broad systemic change at institutional levels. If traditional digital citizenship curricula struggle to address complex social problems, then YPRPT exemplifies one learning experience designed to foster students' critical digital citizenship and encourage them to cultivate justice-oriented civic identities.

Traditional aspects of student engagement have focused on how students feel or think about their instructor, the content being taught, the design of the course, and occasionally the institution, barely extrapolating what truly is meaningful for the student. As documented in various systematic measuring of the student experience, historically, the focus has been on pedagogical approaches, educational practices, and student evaluations of teaching practice (Bowden et al., 2021; Grebennikov & Shah 2013). The traditional practices of higher education institutions must change to keep pace with the expectations of 21st-century learners and the manner they engage in learning.

ResearchYOU! is a co-curricular research experience designed to improve undergraduate student research skills at a Hispanic-serving institution and provide research mentoring for undergraduate students who may not typically have access to social science research experiences. This multi-faculty capacity-building research experience is composed of three components: (1) learning and development of research skills through virtual synchronous workshops, (2) performing research tasks under the mentorship of faculty members, and (3) submission of a research poster presentation for the University Undergraduate Research Day. In this paper, we describe the inaugural year of the program and present preliminary data that point to initial impacts of the program. [For the full proceedings, see ED631897.]

Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher- or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction. [For the full proceedings, see ED623995.]

Mathematics education research has focused on developing teachers' knowledge or other visible aspects of the teaching practice. This paper contributes to conversations around making a teacher's thinking visible and enhancing a teacher's pedagogical reasoning by exploring the use of pedagogical documentation. In this paper, we describe how a teacher's pedagogical reasoning was made visible and highlight aspects of his thinking in relation to his instructional decisions during a series of lessons on division. Implications for professional learning are discussed.

Education policies and innovations that aim to improve instructional quality often fail to produce any meaningful or sustained changes to teaching when implemented at scale because of the significant learning demands they place on the individuals, groups, and organizations that comprise an educational system. In this paper, we describe an implementation resource developed to promote professional learning and cross role discussions about new state mathematics standards and report on the ways educators at different levels of the state system used them. Results demonstrate how implementation resources designed to be a boundary object for educators at multiple levels of an educational system have the potential to support learning and create systemic conditions conducive of change. [For the complete proceedings, see ED630210.]

This study presents an extension of the validity argument for the PCK-Fractions measure. PCK-Fractions is designed to assess the effectiveness of professional experiences in facilitating teachers' pedagogical content knowledge (PCK) for children's fraction reasoning in grades 3-5. We examined data across 101 participants from two Midwest universities including noneducation majors, education majors, and education majors with grade 3 to 5 math field experiences. Results provide additional validity evidence for the PCK-Fractions. Namely, evidence indicates differences in scores between groups of participants--preservice teachers grade 3 to 5 field experience had higher scores than those without, and all preservice teachers had higher scores than non-education majors. [For the complete proceedings, see ED630210.]

Regular expression (regex) coding has advantages for text analysis. Humans are often able to quickly construct intelligible coding rules with high precision. That is, researchers can identify words and word patterns that correctly classify examples of a particular concept. And, it is often easy to identify false positives and improve the regex classifier so that the positive items are accurately captured. However, ensuring that a regex list is complete is a bigger challenge, because the concepts to be identified in data are often sparsely distributed, which makes it difficult to identify examples of "false negatives." For this reason, regex-based classifiers suffer by having low recall. That is, it often misses items that should be classified as positive. In this paper, we provide a neural network solution to this problem by identifying a "negative reversion set," in which false negative items occur much more frequently than in the data set as a whole. Thus, the regex classifier can be more quickly improved by adding missing regexes based on the false negatives found from the negative reversion set. This study used an existing data set collected from a simulation-based learning environment for which researchers had previously defined six codes and developed classifiers with validated regex lists. We randomly constructed incomplete (partial) regex lists and used neural network models to identify negative reversion sets in which the frequency of false negatives increased from a range of 3%-8% in the full data set to a range of 12%-52% in the negative reversion set. Based on this finding, we propose an interactive coding mechanism in which human-developed regex classifiers provide input for training machine learning algorithms and machine learning algorithms "smartly" select highly suspected false negative items for human to more quickly develop regex classifiers. [For the full proceedings, see ED623995.]

Predicting student performance in an academic institution is important for detecting at-risk students and administering early-intervention strategies. We propose a new grade prediction model that considers three factors: temporal dynamics of prior courses across previous semesters, short-term performance consistency, and relative performance against peers. The proposed architecture comprises modules that incorporate the attention mechanism, a new short-term gated long short-term memory network, and a graph convolutional network to address limitations of existing works that fail to consider the above factors jointly. A weighted fusion layer is used to fuse learned representations of the above three modules--course importance, performance consistency, and relative performance. The aggregated representations are then used for grade prediction which, in turn, is used to classify at-risk students. Experiment results using three datasets obtained from over twenty thousand students across seventeen undergraduate courses show that the proposed model achieves low prediction errors and high F1 scores compared to existing models that predict grades and thereafter identifies at-risk students via a pre-defined threshold. [For the full proceedings, see ED623995.]

Knowledge tracing (KT) models are a popular approach for predicting students' future performance at practice problems using their prior attempts. Though many innovations have been made in KT, most models including the state-of-the-art Deep KT (DKT) mainly leverage each student's response either as correct or incorrect, ignoring its content. In this work, we propose Code-based Deep Knowledge Tracing (Code-DKT), a model that uses an attention mechanism to automatically extract and select domain-specific code features to extend DKT. We compared the effectiveness of Code-DKT against Bayesian and Deep Knowledge Tracing (BKT and DKT) on a dataset from a class of 50 students attempting to solve 5 introductory programming assignments. Our results show that Code-DKT consistently outperforms DKT by 3.07-4.00% AUC across the 5 assignments, a comparable improvement to other state-of-the-art domain-general KT models over DKT. Finally, we analyze problem-specific performance through a set of case studies for one assignment to demonstrate when and how code features improve Code-DKT's predictions. [For the full proceedings, see ED623995.]

This research study explores nuances to instructional delivery models beyond the COVID-19 pandemic. A case study that examines the redesign and reimagining of a graduate course to fit the diverse needs and preferences of students. This research was intended to provide insight into the possibilities of redesigning and reimagining graduate level courses for a master of education in educational leadership program at a state university in Louisiana. The research team engaged this case study in order to address the needs of current and prospective students regarding face to face, hybrid models, and online options for course delivery. This case study documents efforts to redesign and reimagine courses using instructional delivery models that meet the diverse needs and preferences of students and potential graduate candidates. While this case study has implications for instructional delivery at every level of education it is a work in progress. It begins to shed light on how graduate programs and course delivery will continue to evolve beyond the COVID-19 pandemic and well into the future.