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This study reviewed the evolving capabilities of hydro-drones, Unmanned Aerial Vehicles (UAVs) which are used for spraying and cleaning with a focus on water management, and it revealed that the unique capabilities of hydro-drones offered the potential to create challenging and rewarding education and training programs, designed to provide new or expanded employment opportunities and related career paths for adventuresome individuals in the growing field of drone technology. Overall, the study found that hydro-drones are having a dramatic transformational impact on many industries, especially their cleaning and sanitizing programs, while creating a demand for employees with the requisite education and training background in drone technology. The proverbial door is opening wide for higher education, trade organizations, and professional associations to develop and to offer innovative programs covering drone technology, management and entrepreneurship. Included in such courses could be added exposure to legal and regulatory compliance, drone safety, and drafting a drone flight plan, as required by the Federal Aviation Authority (FAA).

This study investigated how 155 pre-service teachers solved three pattern generalization problems in a two-part written test and sequenced them for teaching purposes to demonstrate their curricular noticing. Participants' solutions were analyzed using inductive content analysis, which showed that only 8.4% of PSTs produced correct answers to all three problems, and 14.2% proposed the same sequence as the researchers. As criteria for their sequencing, 52.9% used the problem's difficulty, variously associated with visuality, complexity, and feasibility, and 11% used their own understanding of how to solve the problem. It was also suggested that some participants' incorrect problem-solving influenced their curricular noticing. Implications for helping pre-service teachers improve their curricular noticing ability are discussed. [For the complete proceedings, see ED657822.]

This presentation highlights the challenges of establishing a consistent application of the two terms, "threats" and "hazards," in academic courses and professional training, designed for emergency, disaster, and humanitarian management programs offered by public and private higher education, government agencies, and nonprofits. A new approach for viewing threats and hazards, named Energetic Disrupter Models, is presented, based on their ambient sources of energy, specifically potential or kinetic. Examples of threats and their collateral hazards for naturally occurring events are also offered for discussion and discernment. Such examples can provide the ingredients to created dynamic case studies that reimagine threats and hazards and simultaneously incorporate their respective sources of energy, which can create impactful events.

This study employed a discourse analysis methodology to compare Korean bilingual students in first and third grades by examining different functions and forms of translanguaging in Korean heritage language classrooms in the U.S. By identifying linguistic functions and forms of each translanguaging occurrence, the study presents that the bilingual students engaged in translanguaging practices in support of their meaning- and sense-making processes. The comparison findings from constant comparative analyses display that the older graders employed translanguaging in more varied forms and for more various purposes than the younger graders because they recognized when to use which language by regulating a higher level of their cognitive thinking skills. The findings provide pedagogical and methodological implications for educators in bilingual education.

Is it possible to identify instructional practices that have an impact on student learning in mathematics? The study described here is part of ongoing efforts to understand and characterize effective instruction. We drew on the work of several recently developed frameworks for understanding teaching effectiveness to develop a protocol for studying effective instruction that both coordinates and extends existing research in the context of early algebra. Using a largescale study, we characterized effective instruction in this context and documented the impact of such instruction on students' performance using both qualitative and quantitative analyses. Findings suggest that teachers' abilities to take up curriculum openings are important aspects of teaching. Furthermore, the manner with which teachers react to these moments strongly correlates with gains in student performance. [For the complete proceedings, see ED658295.]

In this study, 82 middle and high school teachers engaged with the InSTEP online professional learning platform to develop their expertise in teaching data science and statistics. We investigated teachers' engagement within the platform, aspects of the platform that were most and least effective in building teachers' expertise, and the extent to which teachers' self-efficacy changed. Using mixed methods, we collected, analyzed and integrated multiple data sources. [For the complete proceedings, see ED658295.]

Like K-12 mathematics teachers, for whom effective instruction requires a full suite of pedagogical and mathematical knowledge and skills, Mathematics Teacher Educators (MTEs) also need a professional knowledge base to facilitate the development of future educators capable of supporting K-12 students' mathematical learning. In this paper, we theorize about contributors to the development of a Community of Practice (CoP) who were engaged with developing and implementing a common set of instructional materials. We discuss how using the materials in various contexts and interacting with the user community provided a much-needed network of MTEs focused on enhancing their students' mathematical learning experiences as well as their own professional development in terms of teaching, research, and curriculum development. [For the complete proceedings, see ED657822.]

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.]

This study aimed to build a model to detect the factors to enhance student engagement and learning development in mobile learning during the COVID-19 Pandemic. Data from a total of 400 junior-high-school students were collected in China in the fall semester of 2020, and a large proportion of students preferred accessing their study with cellphones (67.0%) than with computers (11.8%), laptops (11.3%) or pads (10.0%). Exploratory factor analysis and structural equation modeling (SEM) were used for data analysis. The fitness of the items for each scale of the student engagement and learning development indicated a sufficient fit (X[superscript 2][subscript (19)] = 41.252, GIF = 0.974, AGFI = 0.951, CFI = 0.986, RMSEA = 0.054, NNFI = 0.979, IFI = 0.986). The results of SEM analysis show that emotional engagement is the most important factor (r[superscript 2] = 0.859) in the model, and student engagement has a significant positive impact on learning development in mobile learning. The findings of this study provide a good reference for enhancing student engagement or fostering students' learning development in mobile learning. [For the full proceedings, see ED639391.]

Co-curricular service-learning has increasingly attracted participants from students as it can enrich their experience of learning chemistry, among others. An example is "Kimikamahika," wherein chemistry students from a Catholic university in the Philippines volunteer in public schools, orphanages, daycare centers, and local organizations to promote interest in chemistry among children by demonstrating eye-catching experiments. However, studies are few on the lasting impression of co-curricular service-learning among students compared to their peers without such community engagement. Hence, this mixed-methods study assessed the long-lasting impact of "Kimikamahika" on its past volunteers vis-a-vis their peers not part of this service-learning in terms of gains in course-related skills, real-world application, career opportunities, leadership skills, and community engagement. A one-tailed Mann-Whitney U Test ([alpha] = 0.05) of the survey responses revealed significantly higher ratings given by 54 volunteers of "Kimikamahika" in the acquisition of course-related skills (p = 0.033), application of classroom learning to real-world situations (p = 0.045), and participation in community engagement (p = 0.013) against the ratings of 24 peers not involved in this service-learning. A thematic analysis of interviews with thirteen past "Kimikamahika" volunteers further showed the lasting impression of a cocurricular service-learning on their academic, personal, professional, and civic life. [For the full proceedings, see ED654100.]

This paper discusses the steps involve in project development for developing the mobile application, namely Blood Bank Application and developing the convertor for software testing. The project development is important for Computer Science students for them to learn the important steps in developing the application and testing the reliability of the application. The first step involved is the development of the mobile application. Then the convertor is developed to convert the mobile application in ".apk" format into Java program in ".java" format. The java program is then tested under Eclipse environment using JUnit. Finally, the Java program is tested for its capability of generating test cases using SenaTLSParser. The comparison of the results of the time taken to produce test cases is presented using Junit and SenaTLSParser. Based on the results, SenaTLSParser is more reliable compared with JUnit since its response time is less than JUnit. The whole steps involved in this project development are discussed in this paper. [For the complete proceedings, see ED655360.]

Linggle is a pattern-based referencing tool that assists in collocation learning. In this ongoing project, we aimed to improve its performance further. First, many of the example sentences are long and difficult for students to understand, so we used a machine learning method and trained a classifier to help select dictionarylike example sentences. Second, we created a database of 60,270,000 sentences from 4C, S2ORC, and VOA Learning English. We also included Google books for real-time supplements. Then, we applied the classifier to select good example sentences from the database for display. We also limited the number of example sentences displayed for search results to improve users' experiences. Two classes of English as a Foreign Language (EFL) college students (N=51) were invited to use the enhanced tool and filled out a questionnaire. The results showed that the students were positive about Linggle's new interface and the quality of the example sentences. We expect that more EFL learners will benefit from the tool. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]

The colleges and universities in Taiwan are confronted with the following two problems: (1) The average youth (aged 15-29) unemployment rate is more than double the overall unemployment rate; and (2) Mainly affected by the low birth rate, the admission and vacancy rates of colleges and universities have increased, and fierce competition exists among colleges and universities. This paper aims to answer the following two questions raised in Taiwan: (1) Does students' participation in the study opportunities created by university-industry cooperation really help the success rate of future employment? (2) In addition to cultivating cross-discipline talents, how can universities maintain their sustainable competitiveness? Based on a literature review and working experience, the author positively states that internship experience can improve students' employment rates, earnings, and job satisfaction after graduation. After that, several successful internship experiences and governmental promoting measures in Taiwan are mentioned. Four key success factors of internship are also described as follows: a clear structure, meaningful roles, paid internship, and a supportive environment. In terms of the second question, the author argues that the following measures should be taken: (1) Frequent inventory and analysis of the sources and quality distribution of students recruited by every academic unit; (2) Timely analysis of the resource allocation, financial revenue, and expenditure of academic units; (3) Appoint teachers based on expertise, and promote 360-degree feedback in the evaluation of teaching; (4) Strengthen the cultivation of STEM professionals; (5) Develop students' core foundations and transformative competencies; and (6) Appropriately manage world university rankings as needed.

This paper demonstrates how to apply Machine Learning (ML) techniques to analyze student interaction data collected in an online mathematics game. We examined: (1) how different ML algorithms influenced the precision of middle-school students' (N = 359) performance prediction; and (2) what types of in-game features were associated with student math knowledge scores. The results indicated that the Random Forest algorithm showed the best performance in predicting posttest math knowledge scores among the seven algorithms employed. Out of 37 features included in the model, the validity of the students' first mathematical transformation was the most predictive of their math knowledge scores. Implications for game learning analytics and supporting students' algebraic learning are discussed based on the findings.

As teachers begin to construct an understanding of 'inquiry', and incorporate IBL into their classroom practice, they are challenged to be sensitive to the 'constructivist' nature of the CLD. This paper presents a reflection model structured to trigger thinking about beliefs in teaching and learning in order for teachers to re-examine their practice and adopt new pedagogies. The reflections by two secondary school mathematics teachers are presented as they experiment with inquiry-based learning in the CLD. The teachers showed awareness, monitoring and regulation of their teaching practices including new and existing beliefs.

When mathematics educators work towards making mathematics more relevant, they often think about including more real-world applications into mathematics lessons. But what happens when a lesson is devoid of real-world contexts? In what ways can students find it relevant? This study explores how high school students perceived relevance when they were asked to describe their experiences during decontextualized mathematics lessons. Students highlighted how they found certain characteristics of the lessons to be useful in their learning and how they perceived relevance through different feelings experienced in the lessons. This, in turn, broadens our understanding of what relevance means to students. [For the complete proceedings, see ED630210.]

Given the centrality of angle in mathematics curricula and scarcity of research in this area, we investigated 64 PTs' assimilatory domains of angularity by analyzing the angles they indicated when presented with four segments mutually sharing an endpoint. In both interview and written settings, we found PTs were more likely to recognize convex angles than reflex angles. Additionally, they were more likely to assimilate disjoint angles than angles formed via additive angular compositions. In particular, we found that PTs were unlikely to recognize full angles or additive compositions involving reflex angles. We consider future directions and implications. [For the complete proceedings, see ED630210.]

Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student's experience should be personalized based on that outcome. In this paper, we explore a generalizable approach for resolving this issue by personalizing learning using explanations of predictions generated via machine learning explainability methods. We tested the approach in a self-guided, self-paced online learning system for college-level introductory statistics topics that provided personalized interventions for encouraging self-regulated learning behaviors. The system used explanations generated by SHAP (SHapley Additive exPlanations) to recommend specific actions for students to take based on features that most negatively influenced predicted learning outcomes; an "expert system" comparison condition provided recommendations based on predefined rules. A randomized controlled trial of 73 participants (37 expert-system condition, 36 explanation condition) revealed similar learning and topic-choosing behavior between conditions, suggesting that XAI-informed interventions facilitated student statistics learning to a similar degree as expert-system interventions. [For the full proceedings, see ED623995.]

This study investigated how motivational orientations and learning strategies predict ninth graders' non-routine mathematical problem-solving anxiety. Non-routine mathematical problem-solving anxiety classification and prediction were investigated through TwoStep cluster analysis, linear discriminant analysis, and logistic regression. 274 ninth graders participated in the study. The participants were clustered based on their problem-solving achievements and test anxiety levels: high-level and low-level problem-solving anxiety. Extrinsic goal orientation, rehearsal, and peer learning were significant classifiers. Intrinsic goal orientation, self-efficacy, rehearsal, and help-seeking were significant predictors for ninth graders' non-routine mathematical problem-solving anxiety.

Due to COVID-19, all classes in schools have been converted to distance learning using online platforms. However, various problems were raised when converting face-to-face classes to online distance learning. In particular, the difficulties were more pronounced for practical design courses because they include practical experience. In this context, this study focused on actual online distance learning classroom cases in design education. Based on the qualitative case study methodology, four instructors were interviewed about their experience with distance learning courses that actively utilized online platforms for college design courses as research cases. In design education, which aims at interdisciplinary and convergence thinking, online distance learning is meaningful in that it can expand experience and opportunities for the overall formative design through the development of technology. On the other hand, there are clear limitations in terms of practical production activities for existing online distance learning methods. Therefore, it is necessary to explore whether to develop classes based on the recently used effective activities or whether to find new strategies in the case that these limitations are fundamental to online distance learning. [For the complete proceedings, see ED639262.]

This pilot study focuses on a tool called L2L that allows second language (L2) learners to visualise and analyse their Zoom interactions with native speakers. L2L uses the Zoom transcript to automatically generate conversation metrics and its playback feature with timestamps allows students to replay any chosen portion of the conversation for post-session reflection and self-review. This exploratory study investigates a seven-week teletandem project, where undergraduate students from an Irish university learning French (B2) interacted with their peers from a French university learning English (B2+) via Zoom. The data collected from a survey (N=43) and semi-structured interviews (N=35) show that the quantitative conversation metrics and qualitative review of the synchronous content helped raise students' confidence levels while engaging with native speakers. Furthermore, it allowed them to set tangible goals to improve their participation, and be more aware of what, why, and how they are learning. [For the complete volume, "CALL and Professionalisation: Short Papers from EUROCALL 2021 (29th, Online, August 26-27, 2021)," see ED616972.]

Timely analysis of trends and issues in TVE can help TVE stakeholders cope with rather than oppose them. Educating in the direction of the trend and resolving the important issues can maximize TVE's chance of success. The purpose of this paper was to identify trends and issues in the TVE in 10 Indo-Pacific countries. To achieve this purpose, a cross-country analysis with a word cloud analysis was employed. Consequently, the following nine trends were identified: (1) Accelerated adaptation to emerging technologies and the evolution of industry; (2) Improving or diversifying TVE accessibility and increasing the enrollment rate; (3) Enhancing alignment between the TVE and higher education sectors; (4) Promoting employment-based, work-based, or competency-based learning models; (5) Strengthening TVE educators'/trainers' practical skills, industrial working experience, or qualification requirements; (6) Gearing TVE with lifelong learning; (7) Encouraging employer or industry involvement in TVE; (8) Enhancing quality assurance and autonomy in the TVE system; and (9) Providing more career counseling or career exploration. In addition, the following six issues were identified: (1) TVE does not have the same positive public image as academic education; (2) Insufficiency of qualified TVE trainers/teachers; (3) Extreme challenges to teach hands-on skills online; (4) Weak involvement of social partners; (5) Fragmentation of TVET management; and (6) The continued lack of a well-constructed qualification framework and quality assurance system.

Taiwan's central government launched the 2030 Bilingual National Policy in 2018 to develop Taiwan into a Mandarin-English bilingual nation by 2030. It is anticipated that the chain of talent development--education, selection, training, and utilization--should interlock with each other to effectively reach the goal. Being in charge of national examinations, mainly selecting civil servants by conducting Civil Service Examinations (CSEs) and certifying professional and technical personnel by Professional and Technical Examinations (PTEs), the Ministry of Examination (MOEX) is striving to implement the following four measures to meet the policy: (1) Increasing the score share of the English subject in CSEs' general subjects, (2) Adding an English test as an eligibility requirement for taking some CSEs and PTEs, (3) Adding a professional English test in some CSEs and PTEs, and (4) Adding a CSE branch--Consular: English II--for the prospective consular personnel to take a tested-in-English examination. However, these measures are confronted with the main challenge that many stakeholders argue that English is not sufficiently important to be included in or to increase the English component in the CSEs or PTEs for their fields. To resolve the main challenge, it is necessary to not only strengthen communication and advocacy, but also to effectively cultivate English proficiency for all from the education side.

Large language models have recently been able to perform well in a wide variety of circumstances. In this work, we explore the possibility of large language models, specifically GPT-3, to write explanations for middle-school mathematics problems, with the goal of eventually using this process to rapidly generate explanations for the mathematics problems of new curricula as they emerge, shortening the time to integrate new curricula into online learning platforms. To generate explanations, two approaches were taken. The first approach attempted to summarize the salient advice in tutoring chat logs between students and live tutors. The second approach attempted to generate explanations using few-shot learning from explanations written by teachers for similar mathematics problems. After explanations were generated, a survey was used to compare their quality to that of explanations written by teachers. We test our methodology using the GPT-3 language model. Ultimately, the synthetic explanations were unable to outperform teacher written explanations. In the future more powerful large language models may be employed, and GPT-3 may still be effective as a tool to augment teachers' process for writing explanations, rather than as a tool to replace them. The explanations, survey results, analysis code, and a dataset of tutoring chat logs are all available at https://osf.io/wh5n9/.

Solving mathematical problems is cognitively complex, involving strategy formulation, solution development, and the application of learned concepts. However, gaps in students' knowledge or weakly grasped concepts can lead to errors. Teachers play a crucial role in predicting and addressing these difficulties, which directly influence learning outcomes. However, preemptively identifying misconceptions leading to errors can be challenging. This study leverages historical data to assist teachers in recognizing common errors and addressing gaps in knowledge through feedback. We present a longitudinal analysis of incorrect answers from the 2015-2020 academic years on two curricula, Illustrative Math and EngageNY, for grades 6, 7, and 8. We find consistent errors across 5 years despite varying student and teacher populations. Based on these Common Wrong Answers (CWAs), we designed a crowdsourcing platform for teachers to provide Common Wrong Answer Feedback (CWAF). This paper reports on an in vivo randomized study testing the effectiveness of CWAFs in two scenarios: next-problem-correctness within-skill and next-problem-correctness within-assignment, regardless of the skill. We find that receiving CWAF leads to a significant increase in correctness for consecutive problems within-skill. However, the effect was not significant for all consecutive problems within-assignment, irrespective of the associated skill. This paper investigates the potential of scalable approaches in identifying Common Wrong Answers (CWAs) and how the use of crowdsourced CWAFs can enhance student learning through remediation. [This paper was published in: "Proceedings of the Tenth ACM Conference on Learning @ Scale (L@S '23)," July 20-22, 2023.]

Intelligent Tutoring Systems (ITSs) have been developed to provide students with personalized learning experiences by adaptively generating learning paths optimized for each individual. Within the vast scope of ITS, score prediction stands out as an area of study that enables students to construct individually realistic goals based on their current position. Via the expected score provided by the ITS, a student can instantaneously compare one's expected score to one's actual score, which directly corresponds to the reliability that the ITS can instill. In other words, refining the precision of predicted scores strictly correlates to the level of confidence that a student may have with an ITS, which will evidently ensue improved student engagement. However, previous studies have solely concentrated on improving the performance of a prediction model, largely lacking focus on the benefits generated by its practical application. In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence. To that end, we apply a state-of-the-art deep attentive neural network-based score prediction model to "Santa," a multi-platform English ITS with approximately 780K users in South Korea that exclusively focuses on the TOEIC (Test of English for International Communications) standardized examinations. We run a controlled A/B test on the ITS with two models, respectively based on collaborative filtering and deep attentive neural networks, to verify whether the more accurate model engenders any student engagement. The results conclude that the attentive model not only induces high student morale (e.g. higher diagnostic test completion ratio, number of questions answered, etc.) but also encourages active engagement (e.g. higher purchase rate, improved total profit, etc.) on "Santa." [For the full proceedings, see ED607784.]

This study demonstrates a leadership professional development (PD) with a goal to build a coalition for attracting and retaining a diverse STEM teaching workforce in K-12 schools. In 2019, 111 participants of 21 teams from across the US participated in the leadership PD. Each team was comprised of 5-6 teachers and administrators representing their schools, districts, regions or states. The leadership PD enabled teams to share their problem of practice related to diversifying the teaching workforce and exchange ideas to resolve their challenges by working as a team with their matched mentor, who supported the group throughout the year. By the end of the 3-day workshop, each team developed a logic model that they could bring to their home school or district to take actions on promoting a diverse teaching workforce in their regions. In this study, we investigate the participants' attitudes and confidence toward the diversity in education; their thoughts about the leadership PD structure and their learning experiences; and the dynamics of an activity system that works to promote diversity in the teaching workforce, using the lens of the cultural-historical activity theory (CHAT).

We investigated the effects of proximal grouping of numbers, problem-solving goals to make 100, and prior knowledge on students' solution strategies in an online mathematics game. Logistic regression on 857 problem-level data points from 227 middle-school students showed that students were more likely to use productive solution strategies on addition and multiplication problems when proximity supported number grouping, 100 was the problem-solving goal, and students had high prior knowledge. Furthermore, when proximity and number goals did not support problem-solving, students with low prior knowledge were less likely to use productive solution strategies on multiplication problems than students with high prior knowledge. Findings demonstrate that the effects of perceptual and number features on solution strategies vary by students' prior knowledge.

The education of preservice teachers is a complicated process. Working toward a disposition that is inclusionary versus exclusionary is a logical and worthy goal, but that means teacher educators acknowledge the demographics and mindset of those involved in this process. The researchers believe culturally responsive teaching should be woven into teacher preparation and culturally responsive mathematics teaching should drive mathematics instruction. The researchers created specific assignments throughout the program to increase the knowledge, skills and dispositions of preservice teachers (PST). The interventions for this study focused on skills related to cultural and cognitive diversity. The eight specific interventions that were given as assignments will be discussed. Results indicate self-efficacy and use of actual strategies increased after exposure to interventions.

In this study, we investigated how sixth and seventh grade students used CODAP to make sense of roller coaster data while engaged in Exploratory Data Analysis (EDA). Using instrumentation theory, we examined students' instrumentation approaches, as well as the types of instrumental orchestration utilized by teachers as they interacted with student pairs during EDA. [For the complete proceedings, see ED630060.]

This study examines the nascent forms of political conocimiento demonstrated by elementary preservice teachers before and after a series of activities designed to engage them in thinking critically and quantitatively about the impacts of different grading systems. In reflections about their learning, the preservice teachers most frequently raised considerations related to knowledge with students and communities, followed by political knowledge. Many of the preservice teachers anticipated using their new knowledge in the near future to understand the implications of their professors' grading systems and in the more distant future to design grading scales in their own classrooms. A few reflections showed awareness of the tensions involved in grading systems, potentially a sign of developing Nepantla, while others evidenced more binary thinking. [For the complete proceedings, see ED630060.]

The US is experiencing extreme social and political polarization not seen since the Civil War. This divisiveness is causing civil unrest and governmental dysfunction which threatens the stability of the nation. Four major causes of the current state are party realignment, the deregulation of news broadcasting, algorithmic personalization of electronic information, and an unknowing public. Adult education can and should be part of the remedy reducing or eliminating harmful polarization. Knowledge of authoritative systems is key to the solution. To promote such knowledge, adult educators can create practical and theoretical learning experiences about authoritative systems and incorporate such knowledge into existing courses and programs. Breadth of the field should produce myriad ways to do so particular to each educator's practice. [For the complete volume, "American Association for Adult and Continuing Education Inaugural 2020 Conference Proceedings (Online, October 27-30, 2020)," see ED611534.]

Research can be a very isolating experience for Ph.D. students. We created a forum for discussion among Ph.D. students to discuss the doctoral experience and generate ideas surrounding collaboration and dialogue using open space methodology. This paper explores isolation as an element of the social and community aspect of learning in a Ph.D. program. The onset of COVID-19 broadened the scope of our exploration and necessitated new adaptations for discussion and interaction. [For the complete volume, "American Association for Adult and Continuing Education Inaugural 2020 Conference Proceedings (Online, October 27-30, 2020)," see ED611534.]

This paper investigates two specific probabilistic biases which middle graders usually exhibit when reasoning about probability and randomness on assessment items. We discuss how students' reasoning about key probability concepts undergirds statistics literacy related to randomness, independence, and the likelihood of future events based on past results. We examine factors evoking misconceptions and students' (in)consistency in exhibiting them. Findings indicate that misconceptions can be evoked based on three types of factors including (1) students' particular understandings of probability and randomness, (2) general item characteristics, and (3) aspects of probability in items. Moreover, possession of a specific misconception will most likely result in exhibiting the bias again on other occasions including the same evoking factors (consistency). [This paper was published in: "Proceedings of the Satellite Conference of the International Association for Statistical Education (IASE): Statistics Education in the Era of Data Science," edited by R. Helenius and E. Falck, International Association for Statistical Education, 2021, pp. 1-6.]

In 2011, the "Let's Count" professional learning program was developed into an elective distance education subject offered at Charles Sturt University. The resulting subject, EMC101: Let's Count, has been offered every year since 2012, and has to date been completed by 796 students. This paper details the subject design and provides enrolment and evaluation data that attest to the success of the subject. [This paper is the second in a symposium of three papers. For the first paper, "'Let's Count' in an Online Learning Environment", see ED616193. For the third paper, "'Let's Count' and Community Professionals", see ED616191.]

In recent years, the need for English as a foreign language (EFL) education in schools has become a priority worldwide. The aim of our paper is to investigate which countries currently focus on researching motivation to learn EFL and what potential reasons are behind the focus. We performed a topic search of the keywords "EFL" and "motivation" in the "Web of Science" database for 2020. In total, we found 61 Social Sciences Citation Index (SSCI) articles. Asia prevails, especially Eastern Asian Chinese speaking regions (Mainland China, Taiwan, and Hong Kong). Policies in Asian regions such as China and Taiwan highly support EFL. There is the aim to develop Taiwan into a "bilingual nation". Likewise, the European Union promotes the establishment of the so-called European Education Area within which studying and training should be accessible and profitable for people living in the EU. Spain remains the European country with the highest number of EFL motivation publications. There were only a few papers from the Americas. In South America, we see evidence of the beginnings of a CLIL push, which has the potential to lead to expanded EFL motivation research in these previously under researched areas. [For the complete Volume 19 proceedings, see ED613922.]

We investigate encoder-decoder GRU networks with attention mechanism for solving a diverse array of elementary math problems with mathematical symbolic structures. We quantitatively measure performances of recurrent models on a given question type using a test set of unseen problems with a binary scoring and partial credit system. From our findings, we propose the use of encoder-decoder recurrent neural networks for the generation of mathematical multiple-choice question distractors. We introduce a computationally inexpensive decoding schema called character offsetting, which qualitatively and quantitatively shows promise for doing so for several question types. Character offsetting involves freezing the hidden state and top k probabilities of a decoder's initial probability outputs given the input of an encoder, then performing k basic greedy decodings given each of the frozen outputs as the initialization for decoded sequence. [For the full proceedings, see ED615472.]

Recent years have seen significant interest in multimodal frameworks for modeling learner engagement in educational settings. Multimodal frameworks hold particular promise for predicting visitor engagement in interactive science museum exhibits. Multimodal models often utilize video data to capture learner behavior, but video cameras are not always feasible, or even desirable, to use in museums. To address this issue while still harnessing the predictive capacities of multimodal models, we investigate adversarial discriminative domain adaptation for generating modality-invariant representations of both unimodal and multimodal data captured from museum visitors as they engage with interactive science museum exhibits. This approach enables the use of pre-trained multimodal visitor engagement models in circumstances where multimodal instrumentation is not available. We evaluate the visitor engagement models in terms of early prediction performance using exhibit interaction and facial expression data captured during visitor interactions with a science museum exhibit for environmental sustainability. Through the use of modality-invariant data representations generated by the adversarial discriminative domain adaptation framework, we find that pre-trained multimodal models achieve competitive predictive performance on interaction-only data compared to models evaluated using complete multimodal data. The multimodal framework outperforms unimodal and non-adapted baseline approaches during early intervals of exhibit interactions as well as entire interaction sequences. [For the full proceedings, see ED615472.]

Knowledge Tracing (KT) is a task to model students' knowledge based on their coursework interactions within an Intelligent Tutoring System (ITS). Recently, Deep Neural Networks (DNN) showed superb performance over classical methods on multiple dataset benchmarks. While most Deep Learning based Knowledge Tracing (DLKT) models are optimized for general objective metrics such as accuracy or AUC on benchmark data, proper deployment of the service requires additional qualities. Moreover, the black-box nature of DNN models makes them particularly difficult to diagnose or improve when unexpected behaviors are encountered. In this context, we adopt the idea of black-box testing / behavioral testing from Software Engineering and (1) define desirable KT model behaviors to (2) propose a KT model analysis framework to diagnose the model's behavioral quality. We test-run the framework using three state-of-the-art DLKT models on seven datasets based on the proposed framework. The result highlights the impact of dataset size and model architecture upon the model's behavioral quality. The assessment results from the proposed framework can be used as an auxiliary measure of the model performance by itself, but can also be utilized in model improvements via data-augmentation, architecture design, and loss formulation. [For the full proceedings, see ED615472.]

Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers. However, manually labeling educational content is labor intensive and error-prone. To address this challenge, prior research proposed machine learning based solutions to auto-label educational content with limited success. In this work, we significantly improve prior research by (1) expanding the input types to include KC descriptions, instructional video titles, and problem descriptions (i.e., three types of prediction task), (2) doubling the granularity of the prediction from 198 to 385 KC labels (i.e., more practical setting but much harder multinomial classification problem), (3) improving the prediction accuracies by 0.5-2.3% using Task-adaptive Pre-trained BERT, outperforming six baselines, and (4) proposing a simple evaluation measure by which we can recover 56-73% of mispredicted KC labels. All codes and data sets in the experiments are available at: https://github.com/tbs17/TAPT-BERT [This paper was published in: "AIED 2021, LNAI1 2748," edited by I. Roll et al., Springer Nature Switzerland AG, 2021, pp. 408-19.]

By applying a conversation analytic approach to analyze the fine-grained manifestation of authority in classroom interaction, we examine the interaction between a teacher and a student while the student presented a solution at the whiteboard. This case highlights the complexity of shared authority and the ways that authority is construed by discourse practices and negotiations over the ownership of knowledge. We offer a nuanced interpretation of how the teacher and student shared epistemic authority through their joint activity. Further, we argue for the importance of distinguishing teachers' epistemic authority and deontic authority to further our understanding of how a teacher can share authority with students during instruction. [For the complete proceedings, see ED629884.]

In this report, we present how one prospective elementary teacher (PT) engaged in the Ant Farm Task, which we designed to investigate PTs' reasoning about coordinate systems. We highlight the cognitive resources the PT drew upon in solving the task via the establishment of a Cartesian coordination and consider educational implications. [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.]

Researchers and reformers across multiple areas of scholarship have challenged the idea of mathematics as fixed, politically neutral, and value-free. Ethnomathematics has brought attention to the mathematical practices of particular cultural groups that differ from Western ways of mathematical understanding. These practices raise the following question for mathematics education, especially within Indigenous communities: whose knowledge should be taught and from whose perspective? One response to this question is to teach both dominant and non-dominant perspectives on mathematics, which can be considered an "etic-emic" approach to mathematics education. Drawing on the literature on decolonizing studies in education, I offer a theorization of this etic-emic approach in terms of re-mythologizing mathematics, pursuing recognition and reconciliation, and refusing colonization. [For the complete proceedings, see ED629884.]

This preliminary study examined whether distinct student profiles (N = 760) emerged based on their behavioral patterns in an online algebraic learning game. We applied k-means clustering analysis to clickstream data collected in the game and then examined how students' behavioral patterns varied across the clusters using data visualization. The results identified four groups of students based on their in-game behaviors, showing that there was a large variation in their behavioral patterns for engaging with the game.

This investigation hypothesizes that the effects derived from the reintegration of adult wilderness education will connect adults in unique ways to nature landscapes. The study is based on two cases: a five-year wilderness program that the author designed in a remote part of Canada, and a Yellowstone wilderness program that occurs yearly. The results suggest that nature does heighten adult emotive levels and may lead to increased appreciation for nature or altered states of consciousness.

This paper describes the development of a mobile application to engage students in active learning in analytical chemistry. Colorimetry is a topic in analytical chemistry that can effectively be learned through the use of mobile devices to perform colorimetric measurement in the real-life environment. Previous attempts in the development of colorimetry mobile apps have been described in the literatures, however, shortcomings exist in their ease of use for teaching and learning and dissemination. We herein propose a colorimetry mobile app design that allows anyone to download, and easily perform colorimetric measurements and engage in active learning anytime, anywhere. Pedagogical underpinnings of the mobile learning activity and app design will also be discussed. [For the complete proceedings, see ED590271.]

Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels. Our experiments on concept prerequisite relations show our proposed framework can substantially improve the classification performance with the same query budget compared to other baseline approaches. [For the full proceedings, see ED599096.]

Rapid advancements in computing have enabled automatic analyses of written texts created in educational settings. The purpose of this symposium is to survey several applications of computerized text analyses used in the research and development of productive learning environments. Four featured research projects have developed or been working on: (1) equitable automated scoring models for scientific argumentation for English Language Learners; (2) a real-time, adjustable formative assessment system to promote student revision of uncertainty-infused scientific arguments; (3) a web-based annotation tool to support student revision of scientific essays; and (4) a new research methodology that analyzes teacher-produced text in online professional development courses. These projects will provide unique insights towards assessment and research opportunities associated with a variety of computerized text analysis approaches. [This paper was published in: "13th International Conference on Computer Supported Collaborative Learning Proceedings," 2019, pp. 743-750.]

This article draws on social psychology's research into attitudes and values to explain how transfer of writing knowledge may be facilitated through attitude formation and change. The theoretical writing knowledge as stated in Threshold Concepts of Writing explains that writing is a tool that mediates human interactions, enacting and evolving ideologies and identities within activity systems and discourse communities. Such an understanding, we hope, encourages students to see the context-specific nature of writing, and the specific ideologies and beliefs that exist within communities whom students are addressing as they write. I want to propose that if students are to transfer such theoretical writing knowledge across contexts, teachers must: (1) define transfer of writing knowledge as beyond the general definition of repurposing of knowledge, to a more specific list of behaviors such as active reading, questioning, listening, and participation within a discourse community-behaviors which would facilitate understanding of specific writing genres, conventions, and community ideologies, beliefs, and interests; (2) understand that transfer is likely to happen when students have positive attitudes towards both theoretical knowledge and specific behaviors, given Fishbein and Ajzen's Theory of Planned Behavior, which suggests behavior is more likely when individuals believe in the positive consequences of the behavior, the subjective norms of such behavior, and also, in their self-efficacy in overcoming potential obstacles when engaging in the behavior; and (3) encourage students to have positive attitudes by showing how theoretical knowledge and transfer behavior fulfill values. Invoking values (for example, by exemplifying how learning our knowledge and engaging in information-seeking and community participation may contribute to effective writing for one's families, cultures, and communities, thus fulfilling values of security and benevolence from Schwartz's model), may provide students motivation for elaboration and opportunities for self-affirmation, as we encourage positive attitudes and beliefs towards writing theory and transfer behavior. [For the complete proceedings, see ED625798.]