Multiple-choice questions (MCQs) are central to instruction and assessment, with distractors revealing student understanding and misconceptions. However, creating high-quality distractors is time-consuming, especially for emerging domains like K–12 AI education. This study explores using generative AI to support distractor creation in a self-paced online module integrating AI and Algebra 1. Five MCQs were selected to compare distractors written by human developers and ChatGPT, using expert reviews and log data from 80 students. Experts rated human distractors higher overall, though AI ones consistently ranked second. Log analysis showed human distractors drew more initial selections, while students who chose AI distractors spent more time engaging without differences in hint use or revisits. Transition patterns across attempts suggest AI-generated distractors can effectively guide students toward correct answers, highlighting their potential for scalable MCQ design.

Artificial Intelligence (AI) literacy is increasingly recognized as a critical competency for K–12 students, yet little is known about how learners engage with AI-focused modules in virtual school contexts. To address this gap, we designed an online narrative-driven AI literacy module (AI4VS) that integrates AI learning with Algebra 1. In this pilot study, data from 80 secondary school students who completed the 250-minutes module in three weeks were analyzed, including 117,866 system log records (e.g., submissions, clicks) and pre-/post-surveys on mathematics motivation, AI self-efficacy, and AI literacy. Using K-means clustering, we identified four distinct behavioral patterns: reflective learners, low-revision committers, high-frequency trial-and-error learners, and balanced learners. These groups demonstrated different outcomes: while all clusters showed significant improvement in AI self-efficacy, only some showed notable gains in motivation (i.e., low-revision committers and balanced learners) and AI literacy (i.e., balanced learners). The findings underscore the need for tailored scaffolds to better support varied learning strategies and highlight the potential of the AI literacy module in accommodating diverse learner profiles.

While teachers’ discourse is central to shaping student learning in online video-based contexts, little is understood about how specific linguistic features of discourse affect student behaviors and performance. This study examines teachers’ discourse patterns in large-scale online mathematics learning environments and their relationships with student dropout during videos and post-video accuracy. A total of 100 videos (25 topics) were collected from four teachers with differing levels of teaching experience (two senior, two junior). Drawing on systemic functional linguistics, discourse features were analyzed using natural language processing, focusing on process types, participant features, logical relations, and interactional markers. Results showed that senior teachers employed more logical extensions and imperatives but fewer interrogative and inclusive clauses than junior teachers. While dropout rates did not differ significantly, students of junior teachers achieved higher post-video accuracy. Regression analyses revealed that dropout was significantly lower in videos with greater use of relational processes. In contrast, post-video accuracy was positively associated with the use of material, mental, and relational processes, as well as elaboration, enhancement, interrogative, and inclusive features. Negative predictors of accuracy included technical terms, complex noun phrases, extension, and imperative forms.

Scaling high-quality mathematics assessment requires balancing automated generation with pedagogical standards. While Large Language Models show promise for educational content creation, systematic evaluation of their output and integration with human expertise remain underexplored. We propose a Generate–Filter–Edit human–AI pipeline combining GPT-4's generative capacity with expert teacher judgment to produce middle-school mathematics items for large-scale deployment. Analyzing 5,634 GPT-4-generated questions, we found 68.74% ready for immediate use, 19.42% requiring revision, and 11.84% discarded. Through comprehensive statistical significance testing on 378 textual features, we identified key linguistic predictors distinguishing quality levels. Low-quality items exhibit significantly higher paragraph-level textual overlap (Cohen's d up to 0.403) and greater verbosity, while items requiring revision show higher rare vocabulary ratios. Teacher editing behaviors follow a light-touch approach, focusing on mathematical language formalization while preserving content fidelity. Post-deployment engagement analysis revealed significant correlations between textual complexity features and student–chatbot interaction patterns. Our results validate scalable human–AI collaboration for mathematics item development and provide evidence-based frameworks for automated quality assessment and editing support systems.

Creativity is a critical learning outcome in K–12 computer science, yet assessing it at scale remains challenging. Human-scored approaches, such as the Consensual Assessment Technique (CAT), are resource-intensive and prone to rater variability. Leveraging advances in large language models (LLMs), this study investigates whether GPT-4o can reliably assess creativity in student-generated code. We collected 383 flow-based music programs from 194 upper-elementary students (ages 10–12) between 2022 and 2024. Each artifact was rated by five human experts across four dimensions: originality, complexity, efficiency, and emotional expressiveness. We evaluated three prompting strategies: zero-shot, few-shot with theory-driven exemplars (ECD), and few-shot with human-selected examples. Among them, the ECD-based few-shot prompting yielded the best performance, achieving the lowest mean absolute error (MAE = 0.582) and highest agreement within ±1.0 of human scores (82.4%). Zero-shot prompting, while slightly less accurate, achieved the highest correlation with human scores (Spearman's p = 0.53), suggesting its potential for lightweight deployment.

Multiple-choice questions (MCQs) serve as fundamental assessment tools in computing education, where high-quality distractors are critical for evaluating conceptual understanding and debugging skills.While large language models (LLMs) show promise in automating distractor generation, their effectiveness in reasoningintensive programming domains remains understudied.Another challenge is that current evaluation metrics often emphasize surfacelevel semantics rather than the logical reasoning required in programming tasks, limiting their practical utility for educators.To compare AI-generated and human-authored distractors, this study collected 925 MCQs from two online high school courses.The collected data include the question stem, correct answer, and three human-authored distractors for each question.For AI-generated distractor generation, we employed the GPT-4 API through a structured prompt containing: (1) question stem, (2) correct answer, (3) Bloom's taxonomy level, and (4) instructional constraints.To determine the Bloom's taxonomy level for each question, two assessment experts independently classified all questions based on Bloom's taxonomy (Remember, Understand, Apply, Analyze, Evaluate, Create), achieving moderate inter-rater reliability (Cohen's 𝐾 = 0.67).Discrepancies, which occurred in 33% of cases, were resolved by a third expert to ensure accurate cognitive-level categorization.The GPT-4 model generated three plausible distractors per question while maintaining cognitive-level alignment.This study proposes a human-AI collaborative framework to evaluate distractor quality in programming education.Our human-AI collaborative evaluation framework combined human expertise with AI analysis (using GPT-4 and DeepSeek-V3) through a three-phase process: First, human-created and AI-generated distractors were anonymized and randomized.Next, human experts and AI models independently selected the three most pedagogically effective distractors per question based on plausibility and challenge potential.Finally, we implemented a ranking system prioritizing distractors with the highest selection frequency across evaluators, with ties resolved by cross-validator agreement.Results demonstrate that AI-generated distractors achieve comparable quality to human-crafted ones for foundational programming concepts (e.g., syntax recall and basic logic).However, significant gaps emerge in higher-order cognitive domains, particularly

Some online learning platforms frequently recommend educational materials to attract student engagement, with visual elements playing a critical role in capturing attention. To optimize the visual design of mathematical stories, this study examines the relationship between visual features and click frequency, based on log data from a U.S. platform featuring AI-generated mathematical stories for elementary students. Our methodology involves a multi-level visual feature extraction framework, categorizing features into low-, mid-, and high-level. Low-level features capture fundamental visual elements like color, texture, shape, and composition, commonly used for their simplicity. Mid-level features, inspired by psychological and artistic theories, more directly link to emotional impact, including attributes like brightness and contrast. High-level features focus on semantic content, using AI models to extract aesthetic scores and identify entities. Based on the correlation analysis between visual features and clicks, our findings indicate that images featuring characters and natural landscapes positively correlate with student interest, aligning with theories of situational interest. In contrast, images with pronounced brightness contrasts negatively impact engagement, likely due to increased cognitive load. The study highlights the limited influence of mid-level aesthetic features on elementary students' engagement, emphasizing the importance of visual clarity and educational relevance over purely aesthetic considerations.

Despite extensive research on code plagiarism detection in higher education and for programming languages like Java and Python, limited work has focused on K-12 settings, particularly for pseudocode. This study aims to address this gap by building explainable machine learning models for pseudocode plagiarism detection in online programming education. To achieve this, we construct a comprehensive dataset comprising 7,838 pseudocode submissions from 2,578 high school students enrolled in an online programming foundations course, along with 6,300 pseudocode samples generated by three versions of generative pre-trained transformer (GPT) models. Utilizing this dataset, we develop an explainable model to detect AI-generated pseudocode across various assessments. The model not only identifies AI-generated content but also provides insights into its predictions at both the student and problem levels, thus enhancing our understanding of AI-generated pseudocode in K-12 education. Furthermore, we analyzed SHAP values and key features of the model to pinpoint student submissions that closely resemble AI-generated pseudocode. This research offers implications for developing robust educational technologies and methodologies to uphold academic integrity in online programming courses.

Music and computer science (CS) have profound historical and structural connections, with programming music offering a promising avenue for engaging children in CS through creative expression. To foster this engagement, our team developed M-Flow, a flow-based music programming platform designed to introduce students to CS via music. Despite extensive existing research in music and CS education, experience reports and empirical studies on K-12 teachers' implementation and its impact on young kids' learning are limited. Therefore, we recruit elementary school teachers and students with no or limited prior programming experience, introducing them to M-Flow and its curriculum through a professional development workshop, a semester's job embedded support, and classroom implementation. We describe the experiences of teachers as they attempt to integrate music and CS, the challenges they face, and the influence on students' attitudes toward learning computing concepts. Specifically, we reflect on our intervention by conducting a sequential mixed-method evaluation. During the qualitative phase, we collected multiple sources of data from three teachers through focus groups and debriefings after a semester of classroom implementation. Thematic analysis of workshop activities, interviews, and debrief videos revealed three themes with seven sub-themes on teachers' integration of flow-based music programming and two themes with five sub-themes on challenges faced by the teachers. In the quantitative phase, we gathered data on attitudes and self-efficacy from 75 students taught by these teachers. Results indicate that the flow-based music programming environment provided an engaging programming experience for students and significantly increased their self-efficacy towards learning programming.

Background: The United States has made substantial investments to restore the global competitiveness of the semiconductor industry; however, the nation continues to face a shortage of skilled labor in this sector. Despite the importance of this issue, there is limited research examining the barriers in individuals' educational and career choices in the semiconductor industry. To address this gap, our study aims to identify the contextual and psychological factors influencing decisions to pursue academic degrees and careers in semiconductors, using expectancy-value theory as our theoretical framework. Method: We first conducted interviews with engineering students and industry professionals in the semiconductor field to explore potential motivators and barriers. Thematic analysis of the interviews revealed multiple factors related to the utility, cost, and interest value. Based on these findings, we designed a survey and gathered responses from 178 participants, including students, faculty, and industry professionals in the semiconductor field. Findings: The results indicated that utility value (e.g., financial stability) plays a significant role in career aspirations for both industry professionals and students. When making career choices in semiconductors, its relative cost value compared to software engineering (e.g., lower pay, limited remote working) also played an important role. This suggests that semiconductor companies are competing not only with other semiconductor companies but also with other big tech companies, necessitating the provision of a comparable work environment to attract skilled engineers. Furthermore, both students and faculty identified limited lab activities and online resources as major barriers, highlighting the need to enhance accessibility to learning materials. Contribution: This study examines the varied perspectives of students, faculty, and industry professionals concerning the essential factors influencing career aspirations in the semiconductor field. To accomplish this, a survey questionnaire tailored to semiconductor career aspirations was developed, drawing insights from interviews with students and industry experts. The findings provide valuable insights for educational and industry approaches aimed at fostering the future workforce in the semiconductor industry.

The importance of discussing support for learning within online learning communities is widely recognized, yet the diverse user behaviors, especially in the realm of online math learning, are not thoroughly investigated. This work in progress research paper explores the mathematical literacies, and success rates of discussion learning among students taking on different participation roles in an asynchronous online math learning setting. This inquiry is pivotal for comprehending the mechanisms that uphold online learning communities and can provide insights for crafting online discussion activities. This paper employs a mixed-methods approach to analyze big educational data and core-periphery structures within the community. Initially, users are classified into core, periphery, and X-periphery (extra) groups using an extended Surprise detection algorithm, which evaluates interaction quality. The Mann-Whitney U test is applied to assess differences in math literacy and discussion success rates among the groups. The findings reveal that each group is more responsive to its own members, with the core group exhibiting a more balanced response pattern. Notably, the X-periphery group shows the highest success rate in discussions, suggesting that lower activity levels do not compromise communication efficiency. Statistical analysis indicates that while the core and periphery groups have similar levels of math literacy, the X-periphery group excels in all three types of mathematical literacy assessed. These results highlight the importance of considering group dynamics and participation roles when designing online math learning activities to foster effective communication and support within the community. The study's insights into social support traffic offer practical implications for practitioners aiming to enhance the sustainability of online learning communities through tailored discussion activities.

This work-in-progress research paper aims to explore students' dropout behavior during video engagement in online learning platforms. As online learning becomes increasingly popular, analyzing how students engage with video content provides important insights into their learning behaviors. This study explores multiple factors influencing K-12 students' in-video dropout rates in online math education. We examined 34,666,481 log entries from Math Nation, covering 1313 videos and 14,251 students. Using survival analysis, we evaluated how 27 variables, including demographic details, video interaction behaviors, and video characteristics(e.g. length, category), affect in-video dropout. Our findings reveal that video length significantly predicts dropout, with each additional minute increasing the dropout rate by 1.26%. Videos with higher dropout rates often feature more frequent pauses, jumps, and rewatches. The study also highlights that the quality of video content, the creators of the videos, and how students interact with the videos are crucial factors affecting dropout rates. Further research is needed to determine the specific causes of video dropout.

This study examines public sentiment towards AI in education, focusing on the impact of ChatGPT's launch by OpenAI on November 30, 2022. Analyzing around 80,000 Twitter posts from before and after the launch, we conducted a comprehensive sentiment analysis using a fine-tuned BERT, outperforming traditional methods such as VADER and SVM. We applied an RDD to assess the causal impacts of ChatGPT's introduction on public sentiment track sentiment shifts, highlighting how the introduction of AI technologies like ChatGPT has influenced educational discourse. Our findings reveal significant public sentiment changes post-launch, contributing new insights into AI's role in education and public discourse.

Open-ended mathematical problems are a commonly used method for assessing students’ abilities by teachers. In previous automated assessments, natural language processing focusing on students’ textual answers has been the primary approach. However, mathematical questions often involve answers containing images, such as number lines, geometric shapes, and charts. Several existing computer-based learning systems allow students to upload their handwritten answers for grading. Yet, there are limited methods available for automated scoring of these image-based responses, with even fewer multi-modal approaches that can simultaneously handle both texts and images. In addition to scoring, another valuable scaffolding to procedurally and conceptually support students while lacking automation is comments. In this study, we developed a multi-task model to simultaneously output scores and comments using students’ multi-modal artifacts (texts and images) as inputs by extending BLIP, a multi-modal visual reasoning model. Benchmarked with three baselines, we fine-tuned and evaluated our approach on a dataset related to open-ended questions as well as students’ responses. We found that incorporating images with text inputs enhances feedback performance compared to using texts alone. Meanwhile, our model can effectively provide coherent and contextual feedback in mathematical settings.

The significance of nurturing a deep conceptual understanding in math learning cannot be overstated. Grounded in the pedagogical strategies of induction, concretization, and exemplification (ICE), we designed and developed a conversational AI using both rule- and generation-based techniques to facilitate math learning. Serving as a preliminary step, this study employed an experimental design involving 151 U.S.-based college students to reveal students’ attention patterns, technology acceptance model, and qualitative feedback when using the developed ConvAI. Our findings suggest that participants in the ConvAI group generally exhibit higher attention levels than those in the control group, aside from the initial stage where the control group was more attentive. Meanwhile, participants appreciated their experience with the ConvAI, particularly valuing the ICE support features. Finally, qualitative analysis of participants’ feedback was conducted to inform future refinement and to inspire educational researchers and practitioners.

While virtual learning environments (VLEs) are widely used in K-12 education for classroom instruction and self-study, young students’ success in VLEs highly depends on their self-regulated learning (SRL) skills. Therefore, it is important to provide personalized support for SRL. One important precursor of designing personalized SRL support is to understand students’ SRL behavioral patterns. Extensive studies have clustered SRL behaviors and prescribed personalized support for each cluster. However, limited attention has been paid to the algorithm bias and fairness of clustering results. In this study, we “fairly” clustered the behavioral patterns of SRL using fair-capacitated clustering (FCC), an algorithm that incorporates constraints to ensure fairness in the assignment of data points. We used data from 14,251 secondary school learners in a virtual math learning environment. The results of FCC showed that it could capture six clusters of SRL behaviors in a fair way; three clusters belonging to high-performing (i.e., H-1. Help-provider, H-2) Active SRL learner, H-3) Active onlooker), and three clusters in low-performing groups (i.e., L-1) Quiz-taker, L-2) Dormant learner, and L-3) Inactive onlooker). The findings provide a better understanding of SRL patterns in online learning and can potentially guide the design of personalized support for SRL.

Mathematical stories have demonstrated the ability to bolster the motivation and interest of students in learning mathematics, thereby exerting a positive influence on their academic performance. However, due to a lack of adequate resources and the desire to engage students in the creation process, Generative Artificial Intelligence (GAI) is utilized to generate mathematical stories accompanied by images. This study presents a framework for automatic quality assessment to evaluate the coherence of multimodal text and images generated by GAI, as well as the appropriateness of the stories for different grade levels. The dataset comprises mathematical stories generated by GAI for grades 3, 4, and 5, obtained from an American online learning platform, each story consisting of titles, bodies, and image illustrations generated by GP4, images generated by DALL-E3. Initially, a method is devised based on CLIP model and Mini-GPT4 for extracting multimodal semantic features to establish the relationship between text and images in mathematical stories and their generation parameters. Subsequently, mathematical text features are designed, including nine mathematical text attributes and ten traditional text readability indicators, followed by collinearity feature selection and statistical testing. Finally, five machine learning grade regressor for mathematical stories were trained, and the correlation between these 19 features and grades is explored using genetic algorithm-based factor mining and the interpretable artificial intelligence method SHapley Additive exPlanations (SHAP). To further understand advanced text features, the latest natural language processing (NLP) readability indicators are also integrated into the analysis. Through multimodal features, traditional text readability metrics, and NLP readability indicators, this method introduces a novel approach for automatically assessing the quality of GAI-generated multimodal mathematical stories, providing a tool for grade predictor and shedding light on the factors (Image-text relevance and textual features) influencing the grade level of analyzed stories, thereby offering new insights for leveraging GAI in mathematical education.

This research aims to investigate the allocation mechanisms of written positive affective feedback (PAF) in online mathematics assignments provided by teachers, employing multimodal learning analytics. We extract mathematical text features and readability indicators from teacher comments, utilizing collinearity matrices for linear feature selection. Student multimodal response patterns are obtained through clustering. To analyze the teacher comment strategies under different student response patterns, Mann-Whitney U tests were employed to investigate differences in student scores and feedback readability between scenarios with and without PAF. Our findings uncover the linguistic characteristics of teacher-provided PAF and the corresponding strategies they adopt. Teachers are more inclined to offer PAF to K-12 students with higher scores, challenging assignments, and younger ages. The study points out potential imbalances in the allocation of teacher PAF and emphasizes key factors that teachers need to consider when providing PAF. The findings offer new insights for educators to contemplate on designing and implementing more effective PAF strategies.

Students' participation in online discussions often varies over time. Leveraging over three million discussion interactions from an online math learning platform, the current study aims to investigate the sustainability of student participation in an online mathematical discussion board by examining how students evolve from newcomers in the discussion board into old-timers over an academic year. Additionally, it seeks to explore how the timing of students' joining the discussion board and their early technology and social participation behaviors influence their continued participation, based on Communicative Ecology Theory (CET), which conceptualizes the sustainability of an online community as being influenced by factors related to communicating technology, social interactions, and exchanged information. The results revealed that students decreased their participation or even left the discussion board over time, underscoring the need to maintain students' participation in online mathematical discussions. Students' social interactions and their technology use in the online discussion board were found to influence their sustained participation. The timing of students joining and their role as newcomers or old-timers were also found to affect their participation behaviors and sustained participation. Future investigations have been planned to further use machine learning to examine students' discussion content and understand the role of discussion content on students' continued participation.

Participating in online mathematical discussions is considered a beneficial strategy to improve online math learning. However, achieving high levels of interaction in these discussions is uncommon, and maintaining them is often challenging. To shed light on the potential mechanisms to sustain online mathematical discussions, this study draws on Communicative Ecology Theory (CET), which conceptualizes the sustainability of online communities as influenced by technical, social, and discursive factors, to examine how students' technical, social, and content-related participation patterns on an online mathematical discussion board are correlated. The results indicated that students' engagement with the communication tool and the motivation system significantly facilitated their social interactions and enhanced their demonstration of mathematical knowledge, mathematical literacy, and affect control. These findings demonstrate the interplay among the three types of participation patterns and provide insights for educators and designers of educational applications to enhance student participation in online discussions thereby improving the effectiveness of these discussions in online learning. Future investigations are planned to focus on how such interplay varies over time.

Because of the structural parallelisms between music and computing, it has long been suggested that coding music could be a good way for young children to engage in and learn about computer science (CS). Despite these suggestions, coding music has not reached a wider audience of young children, and the approach’s potential to engage them has not been thoroughly demonstrated. To facilitate the adoption of coding music activities, we created M-flow, a flow-based programming platform that allows young children to code music intuitively from the outset. Then, we developed a standards-aligned curriculum that teachers applied in their fourth-grade classrooms. Surveys indicate that children were greatly engaged, the experience successfully exposed them to and increased their self-efficacy toward programming. Our results indicate that with the appropriate coding platform, coding music can be a powerful way to engage children in CS.

Mathematical discussions have become a popular educational strategy to promote math literacy. While some studies have associated math literacy with linguistic factors such as verbal ability and phonological skills, no studies have examined the relationship between linguistic synchrony and math literacy. In this study, we modeled linguistic synchrony and students’ math literacy from 20,776 online mathematical discussion threads between students and facilitators. We conducted Cross-Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The statistical testing result comparing CRQA indices between high and low math literacy groups shows that students with high math literacy have a significantly higher Recurrence Rate (RR), Number of Recurrence Lines (NRLINE), and the average Length of lines (L), but lower Determinism (DET) and normalized Entropy (rENTR). This result implies that students with high math literacy are more likely to share common words with facilitators, but they would paraphrase them. On the other hand, students with low math literacy tend to repeat the exact same phrases from the facilitators. The findings provide a better understanding of mathematical discussions and can potentially guide teachers in promoting effective mathematical discussions.

Numerous studies have successfully adopted learning analytics techniques such as machine learning (ML) to address educational issues. However, limited research has addressed the problem of algorithmic bias in ML. In the few attempts to develop strategies to concretely mitigate algorithmic bias in education, the focus has been on debiasing ML models with single group membership. This study aimed to propose an algorithmic strategy to mitigate bias in a multi-group context. The results showed that our proposed model could effectively reduce algorithmic bias in a multi-group setting while retaining competitive accuracy. The findings implied that there could be a paradigm shift from focusing on debiasing a single group to multiple groups in educational attempts on ML.

To address the challenge of teaching the interdisciplinary foundations of data science in computing, mathematics and statistics, we propose a mathematical logic based framework to seamlessly and coherently integrate these foundations. A 8-week module based on the framework is implemented in a high school. The results show an overall feasibility of the integrated approach.

Abstraction, which is considered the most important computational thinking skill, can be learned from programming or computational thinking learning activities. We implemented a 8-week long course to teach high school students statistics and programming. A pre- and post-test was designed to measure students’ understandings of computing and statistics. This work reports some interesting observations we made on students’ misconception of abstraction while examining students’ responses to test questions.

Computer vision (CV) is a subset of artificial intelligence (AI) that focuses on enabling computers to detect and understand objects from visual stimuli and multimedia. With advances in computational power and open-source libraries, more educators and instructional designers are seeking to capitalize on the perceived benefits of CV. This work-in-progress paper reports the CV approach our research team has developed to explore the usage patterns of neurodiverse learners within a 360-degree spherical video-based virtual reality system.

Discussion forums are important for students’ knowledge inquiry in online contexts, with help-seeking being an essential learning strategy in discussion forums. This study aimed to explore innovative methods to build a peer recommender that can provide fair and accurate intelligence to support help-seeking in online learning. Specifically, we have examined existing network embedding models, Node2Vec and FairWalk, to benchmark with the proposed fair network embedding (Fair-NE). A dataset of 187,450 post-reply pairs by 10,182 Algebra I students from 2015 to 2020 was sampled from Algebra Nation, an online algebra learning platform. The dataset was used to train and evaluate the engines of peer recommenders. We evaluated models with representation fairness, predictive accuracy, and predictive fairness. Our findings suggest that constructing fairness-aware models in learning analytics (LA) is crucial to tackling the potential bias in data and to creating trustworthy LA systems.

Previous research has shown that providing video recommendations to students in virtual learning environments implemented at scale positively affects student achievement. However, it is also critical to evaluate whether the treatment effects are heterogeneous, and whether they depend on contextual variables such as disadvantaged student status and characteristics of the school settings. The current study extends the evaluation of a novel video recommendation system by performing an exploratory search for sources of heterogeneity of treatment effects. This study's design is a multi-site randomized controlled trial with an assignment at the student level across three large and diverse school districts in the southeast United States. The study occurred in Spring 2021, when some students were in regular classrooms and others in online classrooms. The results of the current study replicate positive effects found in a previous field experiment that occurred in Spring 2020, at the onset of the COVID-19 pandemic. Then, causal forests were used to investigate the heterogeneity of treatment effects. This study contributes to the literature on content sequencing systems and recommendation systems by showing how these systems can disproportionally benefit the groups of students who had higher levels of previous algebra ability, followed more recommendations, learned remotely, were Hispanic, and received free or reduced-price lunch, which has implications for the fairness of implementation of educational technology solutions.

Educational researchers have examined artificial intelligence (AI) to automatically support students' learning at a large scale. However, it has been broadly identified that AI models can suffer from algorithmic bias. A recent educational research initiative has shifted to evaluate and address algorithmic bias. Nonetheless, few studies have examined students' perceived fairness towards AI-enhanced systems in education. This study aimed to explore and understand factors that shape students' perceived fairness towards an AI system for math learning at the college level. Specifically, we have conducted a between-subjects randomized experiment with 395 participants to reveal factors that influenced participants' perceptions of fairness. The results showed that students' math anxiety levels and majors could influence participants' reported perceived fairness. In contrast, the outcome favorability of the predictive system did not affect students' perceptions of fairness. Future investigations have been planned to further understand the relationships between the designed manipulations and perceived fairness.

With the advancement of digital technologies, big data and learning analytics have become prevalent in the higher education. Various student-facing systems increased the amount of data available to students, and whether students can use big data and learning analytics effectively will affect their academic success. Most studies, however, have focused on how teachers and administrative personnel use student data to make data-driven instruction and management decisions. As a result, little attention has been given to students' use of relevant data that generated by big data and learning analytics to promote their own learning and growth. This study explored using social cognitive theory to identify possible environmental, personal, and behavioural factors that affect students' data use. We used an online questionnaire that collected 242 completed surveys from Chinese university students. Partial Least Squares (PLS) path modelling was used to analyse the data. The initial findings support the conclusion that university students could be encouraged to effectively use data in three ways: (1) through the promotion of university-wide cultures of data use and sustained improvements in data quality, (2) through the professional development of student data literacy, and (3) through the support of student data autonomy, student data reflectiveness, and students' digital identities.

Novice programming learners encounter programming errors on a regular basis. Resolving programming errors, which is also known as debugging, is not easy yet important to programming learning. Students with poor debugging ability hardly perform well on programming courses. A debugging learning trajectory which identifies learning goals, learning pathways, and instructional activities will benefit debugging learning activities development. This study aims to develop a debugging learning trajectory for text-based programming learners. This is accomplished through (1) analyzing programming errors in a logic programming learning environment and (2) examining existing literature on debugging analysis.

To support online learners at a large scale, extensive studies have adopted machine learning (ML) techniques to analyze students’ artifacts and predict their learning outcomes automatically. However, limited attention has been paid to the fairness of prediction with ML in educational settings. This study intends to fill the gap by introducing a generic algorithm that can orchestrate with existing ML algorithms while yielding fairer results. Specifically, we have implemented logistic regression with the Seldonian algorithm and compared the fairness-aware model with fairness-unaware ML models. The results show that the Seldonian algorithm can achieve comparable predictive performance while producing notably higher fairness.

During disasters, discover people's concerns dynamically is crucial to disaster rescue and relief. In this paper, we propose a social media based framework to analyze people's concerns, to access the importance and to track the dynamic changes of these concerns. To better understand people's concerns across platforms and to monitor the dynamics, we make comparisons between Tweets and news on the mentioned aspects and disclosed some interesting findings. Specifically, we take 2018 Camp Fire, the most destructive wildfire on record in history of California as a case study. We find that despite their keen attentions towards the disaster, social media and news media focus on different aspects of the disaster, so are the contents and dynamic changes of their concerns.

The goal of the present study was to uncover new insights about the learning analytics community by analyzing Twitter archives from the past four Learning Analytics and Knowledge (LAK) conferences. Through descriptive analysis, interaction network analysis, hashtag analysis, and topic modeling, we found: extended coverage of the community over the years; increasing interactions among its members regardless of peripheral and in-persistent participation; increasingly dense, connected and balanced social networks; and more and more diverse research topics. Detailed inspection of semantic topics uncovered insights complementary to the analysis of LAK publications in previous research.

Learning analytics (LA) has invested much effort in the investigation of students' behavior and performance within learning systems. This paper expands the influence of LA to students' behavior outside of learning systems and describes a novel machine learning model which automatically detects students' off-task behavior as students interact with a learning system, ASSISTments, based solely on log file data. We first operationalize social cognitive theory to introduce two new variables, affect states and problem set, both of which can be automatically derived from the logs, and can be considered to have a major influence on students' behavior. These two variables further work as the feature vector data for a K-means clustering algorithm in order to quantify students' different behavioral characteristics. This quantified variable representing student behavior type expands the feature space and contributes to the improvement of the various model performance compared with only time- and performance-related features. In addition, an advanced Hidden Naïve Bayes (HNB) algorithm is coded for off-task behavior detection and to show the best performance compared with traditional modeling techniques. Implications of the study are then discussed.

In this paper we propose an automated strategy to assess participation in a multi-mode math discourse environment called Virtual Math Teams with Geogrebra (VMTwG). A holistic participation clustering algorithm is applied through the lens of activity theory. Our activity theory-informed algorithm is a step toward accelerating heuristic approaches to assessing collaborative work in synchronous technology mediated environments like VMTwG. Our Exploratory findings provide an example of a novel, time-efficient, valid, and reliable participatory learning assessment tool for teachers in computer mediated learning environments. Scaling online learning with a combination of computation and theory is the overall goal of the work this paper is situated within.