As Artificial Intelligence (AI) becomes increasingly integrated into daily life, there is a growing need to equip the next generation with the ability to apply, interact with, evaluate, and collaborate with AI systems responsibly. Prior research highlights the urgent demand from K-12 educators to teach students the ethical and effective use of AI for learning. To address this need, we designed a Large-Language Model (LLM)-based module to teach prompting literacy. This includes scenario-based deliberate practice activities with direct interaction with intelligent LLM agents, aiming to foster secondary school students' responsible engagement with AI chatbots. We conducted two iterations of classroom deployment in 11 authentic secondary education classrooms, and evaluated 1) AI-based auto-grader's capability; 2) students' prompting performance and confidence changes towards using AI for learning; and 3) the quality of learning and assessment materials. Results indicated that the AI-based auto-grader could grade student-written prompts with satisfactory quality. In addition, the instructional materials supported students in improving their prompting skills through practice and led to positive shifts in their perceptions of using AI for learning. Furthermore, data from Study 1 informed assessment revisions in Study 2. Analyses of item difficulty and discrimination in Study 2 showed that True/False and open-ended questions could measure prompting literacy more effectively than multiple-choice questions for our target learners. These promising outcomes highlight the potential for broader deployment and highlight the need for broader studies to assess learning effectiveness and assessment design.

Educational technology (EdTech) solutions have shown promise for disseminating educational opportunities to last-mile learners, particularly in the Global South. Low-infrastructure EdTech as a digital learning resource is especially critical to understand in remote contexts where educational opportunities and resources are limited. Our work investigated insights from 81 learners who engaged with a remote course that provided engineering education through radios and mobile phones in rural Uganda. Findings revealed that the course facilitated goal-driven and practical motivations in safe, adaptable environments. Our work goes beyond the idea that low-infrastructure EdTech can easily facilitate learning, highlighting diverse learner experiences navigating radio and phone use and presenting novel findings on community skepticism towards the course. Our research extends the EdTech and HCI literature by bringing light to the underrepresented voices of last-mile learners, sharing their insights on interacting with low-infrastructure EdTech, and how these insights can guide the design of contextually aligned EdTech.

Generative artificial intelligence (GenAI) is rapidly entering K-12 classrooms worldwide, initiating urgent debates about its potential to either reduce or exacerbate educational inequalities. Drawing on interviews with 30 K-12 teachers across the United States, South Africa, and Taiwan, this study examines how teachers navigate this GenAI tension around educational equalities. We found teachers actively framed GenAI education as an equality-oriented practice: they used it to alleviate pre-existing inequalities while simultaneously working to prevent new inequalities from emerging. Despite these efforts, teachers confronted persistent systemic barriers, i.e., unequal infrastructure, insufficient professional training, and restrictive social norms, that individual initiative alone could not overcome. Teachers thus articulated normative visions for more inclusive GenAI education. By centering teachers’ practices, constraints, and future envisions, this study contributes a global account of how GenAI education is being integrated into K-12 contexts and highlights what is required to make its adoption genuinely equal.

We explore the use of small language models (SLMs) for automatic question generation as a complement to the prevalent use of their large counterparts in learning analytics research. We present a novel question generation pipeline that leverages both the text generation and the probabilistic reasoning abilities of SLMs to generate high-quality questions. Adopting a “generate-then-validate” strategy, our pipeline first performs expansive generation to create an abundance of candidate questions and refine them through selective validation based on novel probabilistic reasoning. We conducted two evaluation studies, one with seven human experts and the other with a large language model (LLM), to assess the quality of the generated questions. Most judges (humans or LLMs) agreed that the generated questions had clear answers and generally aligned well with the intended learning objectives. Our findings suggest that an SLM can effectively generate high-quality questions when guided by a well-designed pipeline that leverages its strengths.

This quasi-experimental study integrates a large language model (LLM) with expert qualitative analysis to examine how instructional design variations in computer-supported collaborative learning (CSCL) shape collaboration in data-centric programming. We collected 73 team transcripts from two contrasting CSCL designs deployed across five course offerings: a closed-ended variant with prescribed solution paths and auto-graded milestones, and an open-ended variant supporting exploratory tasks with multiple valid paths. LLM annotation revealed statistically significant differences in knowledge co-construction patterns: the open-ended design yielded a higher proportion of utterances focused on developing a shared understanding of problems and solutions. Guided by these quantitative results, human experts conducted qualitative coding that confirmed and enriched these findings, showing how open-ended tasks fostered elaborative solution negotiation while closed-ended structures promoted non-elaborative exchanges. Our contributions are: (1) instructional insights for data science education, demonstrating how open-ended CSCL designs better support collaborative sense-making essential for real-world data science projects; and (2) a documented workflow for human-LLM co-evaluation, providing the methodological detail necessary for others to replicate our process and apply it to future studies.

The broad adoption of Generative AI (GenAI) is impacting Computer Science education, and recent studies found its benefits and potential concerns when students use it for programming learning. However, most existing explorations focus on GenAI tools that primarily support text-to-text interaction. With recent developments, GenAI applications have begun supporting multiple modes of communication, known as multimodality. In this work, we explored how undergraduate programming novices choose and work with multimodal GenAI tools, and their criteria for choices. We selected a commercially available multimodal GenAI platform for interaction, as it supports multiple input and output modalities, including text, audio, image upload, and real-time screen-sharing. Through 16 think-aloud sessions that combined participant observation with follow-up semi-structured interviews, we investigated student modality choices for GenAI tools when completing programming problems and the underlying criteria for modality selections. With multimodal communication emerging as the future of AI in education, this work aims to spark continued exploration on understanding student interaction with multimodal GenAI in the context of CS education.

The Doer Effect states that completing more active learning activities, like practice questions, is more strongly related to positive learning outcomes than passive learning activities, like reading, watching, or listening to course materials. Although broad, most evidence has emerged from practice with tutoring systems in Western, Industrialized, Rich, Educated, and Democratic (WEIRD) populations in North America and Europe. Does the Doer Effect generalize beyond WEIRD populations, where learners may practice in remote locales through different technologies? Through learning analytics, we provide evidence from N = 234 Ugandan students answering multiple-choice questions via phones and listening to lectures via community radio. Our findings support the hypothesis that active learning is more associated with learning outcomes than passive learning. We find this relationship is weaker for learners with higher prior educational attainment. Our findings motivate further study of the Doer Effect in diverse populations. We offer considerations for future research in designing and evaluating contextually relevant active and passive learning opportunities including leveraging familiar technology, increasing the number of practice opportunities, and aligning multiple data sources.

Adaptive Experimentation is one of the most promising approaches to support complex decision-making in learning experience design and delivery. This paper reports on our experience with a real-world, multi-experimental evaluation of an adaptive experimentation platform within the XPRIZE Digital Learning Challenge framework, and summarizes data-driven lessons learned and best practices for Adaptive Experimentation in education. We outline key scenarios of the applicability of platform-supported experiments and reflect on lessons learned from this two-year project, focusing on implications relevant to platform developers, researchers, practitioners, and policy stakeholders to integrate Adaptive Experiments in real-world courses.

Expanding access to education in rural African communities remains difficult, largely due to limited internet connectivity. Mobile learning courses delivered via radio and offline mobile phones offer a promising, scalable solution. However, it is challenging to track student engagement in these environments due to the absence of tools that monitor students' interactions with the radio. In this study, we investigate the potential of ''Prize Codes'' -- codes read aloud during broadcasts that students enter via text message -- to serve as a real-time measure of student engagement with mobile-learning broadcasts. Using data from a 2024 implementation of Yiya AirScience, a mobile engineering course in Uganda, we evaluate the validity of Prize Codes as an engagement metric. Specifically, we test whether Prize Code measures (1) demonstrate reliability, with students who enter correct codes in one lesson being more likely to do so in subsequent lessons; (2) demonstrate convergent validity with existing measures of engagement; and (3) demonstrate predictive validity, predicting learning outcomes in the course. Our findings suggest that Prize Codes are a reliable and valid measure of engagement. Prize-Code accuracy demonstrates strong internal consistency (alpha = .97) and moderate test-retest reliability (ICC = .44). The measure aligns closely with synchronous participation (87% agreement, Cohen's kappa = .50), indicating it captures similar engagement patterns. Importantly, students who consistently enter correct Prize Codes perform significantly better on assessments, with Prize Code engagement predicting final exam scores above and beyond other engagement metrics. After establishing the measure's validity, we use it to (1) characterize patterns of engagement with Yiya broadcasts, (2) investigate early engagement with the broadcasts as a predictor of course persistence, and (3) replicate findings about the benefits of learning by doing. This study suggests that Prize Codes can be a feasible, scalable approach for tracking real-time engagement in resource-limited mobile learning settings at scale.

Peer Reviewed

Learning engineering applies data and learning science principles to better understand outcomes and support improvement research. One important approach is A/B testing-common in large software companies and also represented academically at conferences like the Annual Conference on Digital Experimentation (CODE), and the International Consortium for Innovation and Collaboration in Learning Engineering (IEEE ICICLE). Several systems supporting A/B testing in educational applications have arisen recently, including UpGrade, E-TRIALS, and Terracotta. A/B testing can help improve educational platforms, yet there are challenging issues unique to conducting such work in these contexts. In response, a number of digital learning platforms have opened their systems to learning-improvement research by instructors and/or third-party researchers, with specific supports necessary for education-specific research designs. This workshop will explore how A/B testing is conducted in educational contexts, how digital learning platforms are accelerating education research, and how empirical approaches can be used to drive powerful gains in student learning. It will also discuss opportunities for funding to conduct platform-enabled learning engineering.

sponsorship: We would like to thank ZAVO, and Joke Vandepitte in particular, for allowing us to collaborate. We would like to thank FWO by facilitating this interdisciplinary research. Additionally, we would like to thank all participants for their time and valuable insights. This research is part of the research projects funded by KU Leuven (grant C14/21/072) and the Flanders AI Research Program (FAIR). (KU Leuven|C14/21/072, Flanders AI Research Program (FAIR))

Despite the best intentions to support equity with educational technologies, they often lead to a “rich get richer” effect, in which communities of more advantaged learners gain greater benefit from these solutions. Effective design of these technologies necessitates a deeper understanding of learners in understudied contexts and their motivations to pursue an education. Consequently, we studied a 15-week remote course launched in 2021 with 17,896 learners that provided engineering education through a radio and phone-based system aimed for use in rural settings within Northern Uganda. We address shifts in learners’ motivations for course participation and investigate the impact of demographic features and motivations of students on persistence and performance. We found significant increases in student motivation to learn more about and pursue STEM. Importantly, the course was most successful for learners in demographics who typically experience fewer educational opportunities, showing promise for such technologies to close opportunity gaps.

Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for consolidating knowledge and enhancing learning efficacy. However, traditional methods to facilitate reflection often face challenges in personalization, immediacy of feedback, engagement, and scalability. Integration of Large Language Models (LLMs) into the reflection process could mitigate these limitations. In this paper, we conducted two randomized field experiments in undergraduate computer science courses to investigate the potential of LLMs to help students engage in post-lesson reflection. In the first experiment (N=145), students completed a take-home assignment with the support of an LLM assistant; half of these students were then provided access to an LLM designed to facilitate self-reflection. The results indicated that the students assigned to LLM-guided reflection reported somewhat increased self-confidence compared to peers in a no-reflection control and a non-significant trend towards higher scores on a later assessment. Thematic analysis of students' interactions with the LLM showed that the LLM often affirmed the student's understanding, expanded on the student's reflection, and prompted additional reflection; these behaviors suggest ways LLM-interaction might facilitate reflection. In the second experiment (N=112), we evaluated the impact of LLM-guided self-reflection against other scalable reflection methods, such as questionnaire-based activities and review of key lecture slides, after assignment. Our findings suggest that the students in the questionnaire and LLM-based reflection groups performed equally well and better than those who were only exposed to lecture slides, according to their scores on a proctored exam two weeks later on the same subject matter. These results underscore the utility of LLM-guided reflection and questionnaire-based activities in improving learning outcomes. Our work highlights that focusing solely on the accuracy of LLMs can overlook their potential to enhance metacognitive skills through practices such as self-reflection. We discuss the implications of our research for the learning-at-scale community, highlighting the potential of LLMs to enhance learning experiences through personalized, engaging, and scalable reflection practices.

aendees to leave the workshop with a practical understanding of how to engage with learnersourcing.Participants will get hands-on experience with current tools,

Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowledge. To streamline this process for higher-education courses, we employed GPT-4 to generate KCs for multiple-choice questions (MCQs) in Chemistry and E-Learning. We analyzed discrepancies between the KCs generated by the Large Language Model (LLM) and those made by humans through evaluation from three domain experts in each subject area. This evaluation aimed to determine whether, in instances of non-matching KCs, evaluators showed a preference for the LLM-generated KCs over their human-created counterparts. We also developed an ontology induction algorithm to cluster questions that assess similar KCs based on their content. Our most effective LLM strategy accurately matched KCs for 56% of Chemistry and 35% of E-Learning MCQs, with even higher success when considering the top five KC suggestions. Human evaluators favored LLM-generated KCs, choosing them over human-assigned ones approximately two-thirds of the time, a preference that was statistically significant across both domains. Our clustering algorithm successfully grouped questions by their underlying KCs without needing explicit labels or contextual information. This research advances the automation of KC generation and classification for assessment items, alleviating the need for student data or predefined KC labels.

In response to the growing need for frequent, high-quality assessments in the expanding field of online learning and the significant time burden their manual creation places on educators, this study proposes Focal, an end-to-end assessment generation pipeline. Focal employs large language models, notably Text-to-Text Transfer Transformers, fine-trained on diverse learning materials, to generate and evaluate pedagogically sound questions and their corresponding answers. The Focal pipeline is designed to integrate with Learning Management Systems, providing educators an automated means of creating assessments that align with their curriculum. This not only eases the task of creating and evaluating assessments but also frees educators to focus on other crucial responsibilities. The system is domain agnostic and its efficacy is continually improved by training and evaluating it using data from multiple subject areas. By automating the traditionally labor-intensive process of assessment production, Focal aims to increase efficiency in online education and enhance the learning experience for students.

Multiple-choice questions, which are widely used in educational assessments, have the potential to negatively impact student learning and skew analytics when they contain item-writing flaws. Existing methods for evaluating multiple-choice questions in educational contexts tend to focus primarily on machine readability metrics, such as grammar, syntax, and formatting, without considering the intended use of the questions within course materials and their pedagogical implications. In this study, we present the results of crowdsourcing the evaluation of multiple-choice questions based on 15 common item-writing flaws. Through analysis of 80 crowdsourced evaluations on questions from the domains of calculus and chemistry, we found that crowdworkers were able to accurately evaluate the questions, matching 75% of the expert evaluations across multiple questions. They were able to correctly distinguish between two levels of Bloom's Taxonomy for the calculus questions, but were less accurate for chemistry questions. We discuss how to scale this question evaluation process and the implications it has across other domains. This work demonstrates how crowdworkers can be leveraged in the quality evaluation of educational questions, regardless of prior experience or domain knowledge.

The first annual workshop on Learnersourcing: Student-generated Content @ Scale is taking place at Learning @ Scale 2022. This hybrid workshop will expose attendees to the ample opportunities in the learnersourcing space, including instructors, researchers, learning engineers, and many other roles. We believe participants from a wide range of backgrounds and prior knowledge on learnersourcing can both benefit and contribute to this workshop, as learnersourcing draws on work from education, crowdsourcing, learning analytics, data mining, ML/NLP, and many more fields. Additionally, as the learnersourcing process involves many stakeholders (students, instructors, researchers, instructional designers, etc.), multiple viewpoints can help to inform what future student-generated content might be useful, new and better ways to assess the quality of the content and spark potential collaboration efforts between attendees. We ultimately want to show how everyone can make use of learnersourcing and have participants gain hands-on experience using existing tools, create their own learnersourcing activities using them or their own platforms, and take part in discussing the next challenges and opportunities in the learnersourcing space. Our hope is to attract attendees interested in scaling the generation of instructional and assessment content and those interested in the use of online learning platforms.

While data science education has gained increased recognition in both academic institutions and industry, there has been a lack of research on automated coding assessment for novice students. Our work presents a first step in this direction, by leveraging the coding metrics from traditional software engineering (Halstead Volume and Cyclomatic Complexity) in combination with those that reflect a data science project’s learning objectives (number of library calls and number of common library calls with the solution code). Through these metrics, we examined the code submissions of 97 students across two semesters of an introductory data science course. Our results indicated that the metrics can identify cases where students had overly complicated codes and would benefit from scaffolding feedback. The number of library calls, in particular, was also a significant predictor of changes in submission score and submission runtime, which highlights the distinctive nature of data science programming. We conclude with suggestions for extending our analyses towards more actionable intervention strategies, for example by tracking the fine-grained submission grading outputs throughout a student’s submission history, to better model and support them in their data science learning process.

While generating multiple-choice questions has been shown to promote deep learning, students often fail to realize this benefit and do not willingly participate in this activity. Additionally, the quality of the student-generated questions may be influenced by both their level of engagement and familiarity with the learning materials. Towards better understanding how students can generate high quality questions, we designed and deployed a multiple-choice question generation activity in seven college-level online chemistry courses. From these courses, we collected data on student interactions and their contribution to the question-generation task. A total of 201 students enrolled in the courses and 57 of them elected to generate a multiple-choice question. Our results indicated that students were able to contribute quality questions, with 67% of them being evaluated by experts as acceptable for use. We further identified several student behaviors in the online courses that are correlated to their participation in the task and the quality of their contribution. Our findings can help teachers and students better understand the benefits of student-generated questions and effectively implement future learnersourcing activities.

As the media landscape is increasingly populated by less than reputable sources of information, educators have turned to argument evaluation training as a potential solution. Unfortunately, the bias literature suggests that our ability to objectively evaluate an argument is, to a large extent, determined by the relationship between our own beliefs and the beliefs latent in the argument we are evaluating. If the argument supports our worldview, we are much more likely to overlook logical errors. Teachers recognize this need to adapt argument evaluation instruction to the specific beliefs of students. For instance, a teacher might intentionally assign a student an argument that the student disagrees with. Unfortunately, this kind of value-adaptive instruction is infrequent due to its unscalability. We propose a novel method for data-driven value-adaptive instruction in instructional technologies. This method can be used to combat bias in real-world contexts and support human reasoning during media consumption.

Assigning a set of hypothesized knowledge components (KCs) to assessment items within an ed-tech system enables us to better estimate student learning. However, creating and assigning these KCs is a time-consuming process that often requires domain expertise. In this study, we present the results of crowdsourcing KCs for problems in the domain of mathematics and English writing, as a first step in leveraging the crowd to expedite this task. Crowdworkers were presented with a problem and asked to provide the underlying skills required to solve it. Additionally, we investigated the effect of priming crowdworkers with related content before having them generate these KCs. We then analyzed their contributions through qualitative coding and found that across both the math and writing domains roughly 33% of the crowdsourced KCs directly matched those generated by domain experts for the same problems.

We demonstrate that, by using a small set of hand-graded student work, we can automatically generate rubric criteria with a high degree of validity, and that a predictive model incorporating these rubric criteria is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing the more open-ended assignments students encounter in introductory programming environments like Alice. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem - a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric criteria using low-level data from the Alice programming environment.

Many introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student program states at various time points throughout the course, and integrates the resulting predictive models into an instructor dashboard. The effectiveness of the dashboard is evaluated by measuring how well the dashboard analytics correctly suggest that the instructor help students classified as most in need. Finally, we describe a method of matching low-performing students with high-performing peer tutors, and show that the inclusion of peer tutors not only increases the amount of help given, but the consistency of help availability as well.

K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis- and data-driven approaches to discern students' programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.

This workshop will explore community based repositories for educational data and analytic tools that are used to connect researchers and reduce the barriers to data sharing. Leading innovators in the field, as well as attendees, will identify and report on bottlenecks that remain toward our goal of a unified repository. We will discuss these as well as possible solutions. We will present LearnSphere, an NSF funded system that supports collaborating on and sharing a wide variety of educational data, learning analytics methods, and visualizations while maintaining confidentiality. We will then have hands-on sessions in which attendees have the opportunity to apply existing learning analytics workflows to their choice of educational datasets in the repository (using a simple drag-and-drop interface), add their own learning analytics workflows (requires very basic coding experience), or both. Leaders and attendees will then jointly discuss the unique benefits as well as the limitations of these solutions. Our goal is to create building blocks to allow researchers to integrate their data and analysis methods with others, in order to advance the future of learning science.

Learning Analytics courses and degree programs both on-and offline have begun to proliferate over the last three years. As a result of this growth in interest from students, university administrators, researchers and instructors we believe it is a good time to review how these educational efforts are impacting the field, how synergy between instructors might be developed to greater serve the field and what kinds of best practices could be developed.

Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.

This study examines how accurately individual student differences in learning can be predicted from prior student learning activities. Bayesian Knowledge Tracing (BKT) predicts learner performance well and has often been employed to implement cognitive mastery. Standard BKT individualizes parameter estimates for knowledge components, but not for learners. Studies have shown that individualizing parameters for learners improves the quality of BKT fits and can lead to very different (and potentially better) practice recommendations. These studies typically derive best-fitting individualized learner parameters from learner performance in existing data logs, making the methods difficult to deploy in actual tutor use. In this work, we examine how well BKT parameters in a tutor lesson can be individualized based on learners' prior performance in reading instructional text, taking a pretest, and completing an earlier tutor lesson. We find that best-fitting individual difference estimates do not directly transfer well from one tutor lesson to another, but that predictive models incorporating variables extracted from prior reading, pretest and tutor activities perform well, when compared to a standard BKT model and a model with best-fitting individualized parameter estimates.

We examine a large dataset collected by the Marmoset system in a CS2 course. The dataset gives us a richly detailed portrait of student behavior because it combines automatically collected program snapshots with unit tests that can evaluate the correctness of all snapshots. We find that students who start earlier tend to earn better scores, which is consistent with the findings of other researchers. We also detail the overall work habits exhibited by students. Finally, we evaluate how students use release tokens, a novel mechanism that provides feedback to students without giving away the code for the test cases used for grading, and gives students an incentive to start coding earlier. We find that students seem to use their tokens quite effectively to acquire feedback and improve their project score, though we do not find much evidence suggesting that students start coding particularly early.

Automatically-tested online programming exercises can be useful in introductory programming courses as self-tests to accompany readings, for in-class assessment, for skills development, and to provide additional practice for students who need it. CloudCoder (http://cloudcoder.org) is an effort to build a community based on an open-source programming exercise system (currently supporting C, Java, and Python) tightly integrated with a repository of freely-redistributable programming exercises written and used by members of the community. The goal of the project is to make programming exercises easy and free to incorporate into any programming course.

W This panel is proposed as a means of promoting mutual learning and continued dialogue between the Educational Data Mining and Learning Analytics communities. EDM has been developing as a community for longer than the LAK conference, so what if anything makes the LAK community different, and where is the common ground?

We have developed a tool for generating hints within computer-aided instructional tools based on a corpus of student work. This tool allows us to select source problem solutions that match the current user solution and generate hints based on next problem steps that are most likely to lead to a successful solution. However, within such a tool it is possible to generate hints that did not turn out to be useful in the source problem solution. Therefore, we have proposed a metric to measure and integrate a “utility” function to choose source material for hint generation. In this paper we present our metric and an experiment to investigate its use on real data from a logic proof tutorial.

Wireless sensor networks (WSN) are a subset of wireless networking applications focused on enabling sensor and actuator connectivity without the use of wires. Energy consumption among the wireless devices participating in these networks is a major constraint on the deployment for a broad range of applications enabled by WSNs. This paper introduces, for the first time, a novel methodology based on predictive protocol management with contingency planning (PPM and CP). This approach allows efficient update of the WSN operational mode in order to optimize the energy utilization based on the time varying characteristics of the radio-frequency (RF) in which the network operates.