Eye tracking provides a marker of attention. In the educational context, such behavior can be harnessed to understand learning behaviors. However, a technology framework that captures and utilizes such multimodal indicators in educational activities is lacking. This paper presents LA-ReflecT, a platform integrating multimodal data for micro-learning activities. Teachers can author learning tasks and enable tracking eye fixation behaviors. A web camera-based eye-tracking function captures the gaze data while attempting the learning task. Learners can control the settings to stop or pause recording. We present data-driven services such as visualizing gaze attention heatmap and genetic algorithm-based group formation. A classroom study with 41 students illustrates using the proposed framework in an authentic context. Data collected is analyzed to answer an initial research question regarding the correlation between the heterogeneity of the click and gaze patterns in a learning task. The work is open for a demo.

Activities that engage learners to articulate their answers often make them reflect. However, evaluating such activities and providing feedback is time-consuming for teachers. For text analysis, various data-driven indicators, such as cohesion and coherence, evaluate linguistic measures and the semantic understanding of artefacts created. However, for drawing-based activities, defining such indicators is still underexplored. In this research, we conducted a draw-and-write activity that engaged students to express their understanding of a concept through writing and drawing. The question was, “What is data science?”. The human raters analyzed the artefacts generated (n=40), and then a learning analytics approach was taken to define data-driven indicators. The study proposes a data processing pipeline involving a large language model (LLM) and defines indicators to understand the coherence of written text and drawn diagrams. Further, a clustering analysis of the collected artefacts highlighted differences in the participants' expressions of data science (task context). The discussion compares automated and human classification and its implications for assessment and feedback. Future work aims to integrate the pipeline in an online learning environment that affords drawing and text input from the learners.

This study investigates the transferability of Self-Direction behavior across different contexts and phases of learning using the GOAL system. Self-directed learning (SDL) is crucial for lifelong learning. It is significantly influenced by Self- Direction Skills (SDS), a meta-skill that is said to be transferable across different contexts, including the ability to identify learning needs, set goals, select strategies, and evaluate outcomes. Utilizing log data collected from Japanese junior high schools and analyzed using the iSAT system, we explored how Self-Direction behavior acquired in one context can be transferred to another and how these skills vary across the SDL phases. The results indicated that the Self-Direction behavior transferred between different activities and phases. In addition, the way of transfer is suggested to vary from phase and context. This study provides useful insights for the design and guidance of SDL support systems in educational programs. It suggests that it is important for educators to identify factors that facilitate the development and transfer of SDS.

In 2018, OpenAl introduced the first version of the Generative Pre-trained Transformer (GPT), revolutionizing the future of Large Language Models (LLMs). This model demonstrated the potential of pretraining large-scale models with vast text data and then fine-tuning them for specific tasks to the public. LLMs have quickly penetrated educational environments, aiding students from various disciplines in tasks ranging from initiating research to drafting essays. While the latter may breach academic integrity, the former is highly beneficial, especially for exploring new areas or ideas. Comparatively, the user interactions with GPT might resembles initially with that of search engines, despite technological differences, as both provide answers to queries, often reflecting archived as well as mainstream views. The historical evolution of search engines, from Archie's database matching to Google's relevance-based ranking, highlights similar ethical considerations faced by both technologies. The development of search engines underscored the importance of accessible information, a principle equally relevant to GPT and LLMs today. This paper is written in light of recent coming into force of European Union's legal framework on artificial intelligence for the purpose of examining adoption of LLMs in classrooms, and argues for balanced regulations across jurisdictions that acknowledge both the immense educational potential of LLMs and the need for adherence to legal and ethical standards.

This study looks at learning habits of temporal regularity in learning activities. Building such habits involves learners' regulation of their behaviors and requires learning strategies for time management, which is a cornerstone of self-regulated learning (SRL). Given the importance of habit-building in education, Learning Analytics (LA) techniques have been applied to various long-term supports by monitoring learners' habitual behaviors from the trace data. However, building a learning habit does not always mean the productive use of time. Scant supports attend to recommending learners by building which habit can improve their learning productivity. Hence, this study proposes recommendations for productive learning habit-building from learning logs. We focus on the context of English reading in a Japanese junior high school and design an algorithm to compute a recommended learning time slot. Furthermore, we collect learners' perceptions of their productivity and learning status at different times of the day. The comparison between self-report and log data presents that learners are not aware of their learning as the detection from their learning logs. This implies the potential of the proposed recommendations for facilitating learners to build productive learning habits. Specifically, our study can suggest an optimal time in learning plans and provide learners with a sustainable cue to automate learning behaviors from long-term perspectives. By building productive learning habits, learners can become more engaged in their studies as well as lead more balanced lives.

Abstract: In this we focus on critical thinking activities conducted in an online environment and interaction markers based on the data collected from those activities. The work is based on the ENACT framework. We conducted an empirical study to understand the clusters of critical thinkers based on Performance and interactional marker values of the participants (n=37). The highlights the definitions of the interaction markers and conducts an clustering analysis of the of Critical Thinking patterns that emerged. The results show two clusters with similar critical thinking outcome performance but different action patterns. We discuss the need of further empirical evidence relating learning effect on the actions and Critical Thinking.

Abstract: Engineering design is an important aspect of engineering education. The essence of engineering design process has been conveyed to students in diverse ways such as formal capstone and cornerstone projects or informal processes such as hackathons. The interdisciplinary nature of engineering design projects often prove challenging to students in multiple ways. As informal opportunities, hackathons have the potential to acquaint students with several skills key to interdisciplinary engineering design. This paper investigates the contribution of one such hackathon — a medical device innovation hackathon, in supporting student understanding and learning of engineering design process. Specifically, the examines the influence embodied cognition plays on supporting student understanding of an engineering design problem that cuts across multiple disciplines. In the case study, we describe an episode where a team of students go through the gradual process of comprehending the design problem along with the accompanying design complexities through descriptive narration, and simulation.

This paper explores the dynamics of learning interactions between practitioners (those learning skills for real-world activities, sports trainee), and facilitators (those guiding the learning process, sports coach), with a focus on cognitive engagement in adaptive psychomotor learning contexts. Furthermore, this paper examines how to establish an appropriate environment for replicating tangible activities, such as creating optimal conditions for learning how to move in sport scenarios. In particular, we explore how to personalize psychomotor learning approaches through Learning Management Systems (LMS) where the personalization of the learning of motor skills is driven by the Sensing, Modeling, Design and Delivery (SMDD) process model that is based on Artificial Intelligence (A1) support, and the optimization of the learning workflow is managed by the Learning Analytics' enhanced Reflective Task (LA-ReflecT) platform integrated in Moodle LMS.

Self-directed learning (SDL) is considered a crucial skill for 21st-century learners, promoting personalized and responsive educational experiences. This study explores the untapped potential of self-reflection, particularly in e-learning environments. The research focuses on self-reflection notes, which contain strategies past students adopted when facing different situations or challenges. These notes can help current or future students facing similar situations. In this study, students take tests weekly and leave their self-reflection notes after tests. In these notes students recorded their feelings and issues, offering perspectives and insights that experts might overlook or misunderstand in some details, thus failing to provide appropriate assistance. Extracting and categorizing information from self-reflection notes is crucial to further utilize this data. Our research introduces a machine learning-based approach that effectively classifies these self-reflection notes such as cognitive, metacognitive, experiential, and irrelevant text. We compare the performance of BERT, based on the transformer architecture, with traditional machine learning classifiers such as Support Vector Machines (SVM) and Random Forests (RF). Additionally, we enhanced the BERT model by training it on synthetic data generated through GPT-4 and employing a hybrid loss combining Supervised Contrastive Learning (SCL) and Cross-Entropy (CE) to improve classification capabilities. Our results indicate that the BERT model, enhanced with advanced training techniques, outperforms traditional models in classifying learning strategies from self-reflection notes. This study not only advances the understanding of SDL in online learning environments but also demonstrates the potential of tailored machine-learning solutions to foster more effective and adaptive learning strategies.

The paper focuses on the optimized use of “nudges” in an online learning platform by undergraduate students during a semester-long elective course. The study involved in a compelling exercise in which students from different academic years participated in co-designing nudges for an online learning platform to prompt behavior changes. After analyzing the student responses on designed nudges on when these nudges should be used in varying reading rates, they were categorized as confront, social, and deceive. In self-directed learning, the data analysis trends confront and social category nudges were the most preferred, while deceive category nudges were the least popular i.e. The confront category and social category nudges were found more effective than deceive category nudges for behavior change.

Educational explainable AI (XAI) applications are gaining research focus and have distinct needs in the domain of Education. This research presents Educational eXplainable AI Tool (EXAIT), a system for math quiz recommendations, along with an explanation. EXAIT was implemented in a Japanese public high school where students received the top 5 math problems based on Bayesian Knowledge Tracing (BKT) algorithm in a learning analytics dashboard. It aimed to help them complete their summer vacation assignments having 240 questions. On click, the students were redirected to an eBook platform to submit their accuracy and confidence level in each problem. We conducted a study with a quasi-experimental design and divided into 3 groups based on compliance of use. RecoExp group received and used explanations regarding why an item was recommended and how it aims to maximize learners' knowledge-gaining path. RecoCon was the control group that received just the recommendations and used it and RecoNone group did not use the system at all during the time period. We provide a framework to analyze learning logs from EXAIT and extract emerging self-directed learning patterns. Analyzing 222 students' EXAIT logs, we found learners who had checked explanations while selecting recommendations had significantly higher performance. Further differential process mining highlighted significant active daily engagement transitions of the RecoExp group in the self-directed activity.

In terms of the learning logs accumulating from real-world educational activities, techniques of learning analytics help understand the characteristics of learners' behaviors. However, extracting learning habits from log data and data-driven support for building learning habits have not yet attracted much attention. Therefore, this research proposes the approach of “Learning Habits Mining,” which aims to extract the types and stages of learning habits from learners' daily learning logs and support learners to build learning habits with data-driven methods. We identify two contributions of this research. First, this research reveals the learning habits of K12 learners and provides an approach to trace the process of building learning habits automatically. Second, this research proposes interventions to the data-driven support for building learning habits so that learners can build learning habits based on evidence derived from learning logs.

Peer evaluation of individual or group work is often adopted in team-based learning design. However, some raters may not take the evaluation process seriously and exhibit behaviors such as using the same score, rushing through evaluations, or not evaluating during the presentation. This study investigates the issue of unserious peer evaluation in group presentations, focusing on their behavior patterns. Using evaluation behavior analysis indicators, we identified unserious raters who exhibited low reliability in the peer evaluation process. Further, we conducted a preliminary analysis to detect unserious raters based on learner model data available before the peer evaluation process. This information can assist teachers in providing personalized prompts and interventions prior to the peer evaluation process, thus enhancing the evaluation quality of these students with timely prompts to them.

This paper reports an innovative data capturing pipeline for learner modeling by integrating learning logs with physiological and environmental sensor data. The learning logs are collected from BookRoll, an ebook reader. Wearable device signals consist of the user’s affective state from a new version of the eye movement tracking device JINS Meme. An Omron sensor was used to collect environmental data like temperature, noise level, humidity, and luminescence. The architecture of the data collection and its potential are presented in this paper: As a pilot study participants did calculation and comprehension tasks in the ebook reader in two environmental conditions: one within a research lab and one at a cafe to simulate self-study environments. The data from the sensors were collected and synchronized to provide descriptive statistics.

Utilization of Information and Communication Technology (ICT) has been penetrated not only in regular classes, but also in resource rooms, or we call Special Needs Class (SNC), in Japan. Its study has been a popular research target among stakeholders for special needs education, however, there is little reported on discussing how log data can be used to support teaching and learning for children who need special support. In this paper, we propose implementation of using BookRoll (BR), a learning analytic enhanced e-book in SNC. For a pilot study, three participants who attend SNC were selected from an elementary school. Their handwritings with a touch-pen left in a memo function in BR were visible as logs in an analysis tool to illustrate the outcomes of their performance which enabled to trace their learning difficulties. Parents’ intervention as limitation and introducing Learning Evidence Analysis Framework (LEAF) system and learning support for children with special needs from the field of educational learning analytics are suggested.

Self-directed learning (SDL) is an important skill in the 21st century, while the understanding of its process in behavior has not been well explored. Analysis of the sequential behavior patterns in SDL and the relations with students’ academic performance could help to advance our understanding of SDL in theory and practice. In this study, we mined the behavioral sequences of self-directed extensive reading from students’ learning and self-directed behavioral logs using differential pattern mining technique. Furthermore, we built models to predict students’ academic performance using the conventional behavior frequency features and the behavior sequence features. Experimental results identified 14 sequential patterns of SDL behaviors in the high-performance student group. The prediction model revealed the importance of sequential patterns in SDL behavior, which was built with an acceptable AUC. These findings suggested that several SDL strategies in behavior contribute to students’ academic performance, such as analysis learning status before planning, planning before learning, monitoring after learning.

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.

Self-regulation in learning involves planning and utilizing different shared resources. This study investigates learning strategies of different student groups when they are accessing course materials in digital medium - one group is the students with high academic performance, the other is the students with low academic performance. We analyze data of 116 students from a mathematics course in a junior high school. Using the differential pattern mining technique, we highlight underlying course content accessing patterns from the learning log collected by an e-book system, BookRoll.

ENaCT is a framework for the design and analysis of critical thinking environments based on 4E cognition perspectives. In this paper, we describe a web-based critical thinking environment designed to implementœ the ENaCT framework. When users perform a critical thinking task in the environment their interaction logs are captured. We report on a pilot study with undergraduate participants and analyse how participants used the affordances in the environment as they performed the critical thinking task. One case of task-related behaviours (high activity) is elaborated to highlight the current possibilities of the system and discuss implications for redesign.

In this paper, we propose an interactive drawing based activity in BookRoll, an e-book-based teaching-learning platform. Participants who were researchers drew an overview of their research as a handwritten memo. We demonstrate the affordances of the learning analytics (LA) enhanced platform to facilitate practice and research of such a drawing activity focused on a higher-order thinking task to organize knowledge (research overview in this case). We share the multi-modal data of 4 participants to illustrate the outcomes of the activity and their perception of the activity. An initial analysis of the handwritten memo is visualised. Further reflections show how the use of learning analytics-based platforms can enrich the learning design of a knowledge organisation task.

Jigsaw learning is one of the popular cooperative learning methods which has been utilized in many English as Foreign Language (EFL) classrooms. In recent years, utilization of technology in education has accelerated. In this study, Jigsaw+, a new jigsaw learning approach with an e-book reader, was proposed to explore how the learning design can be applied for English vocabulary acquisition, reading comprehension, and students' engagement in group activities. A quasi-experimental study was conducted at a high school in Japan for advanced and standard class students. Participants read and comprehend a story in English through BookRoll, an e-book platform with an analysis tool embedded. Two research questions were investigated; 1) to what extent did Jigsaw+ affect students' English vocabulary knowledge and reading comprehension? and 2) to what extent did Jigsaw+ promote students' reading engagement in the jigsaw group activities? The results revealed that Jigsaw+ learning tasks assisted to improve students' vocabulary and reading comprehension for both the advanced and standard groups. Moreover, it facilitated students' engagements in activities. Some limitations of the study are also discussed.

Digitized learning materials are a core part of modern education and also can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open ebook test from the perspective of performance and identifying strategies that are used by both high and low performing learners. We create models to predict the performance of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that 1) strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment; and 2) low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment.

Self-direction skills in the context of learning can be supported with data in this digital era. This study analyzes the behaviors of learners during a self-directed reading and summarization task. Our work investigates an undergraduate course (n=72) where students worked on a reading and summarizing assignment while planning and monitoring the task in GOAL, a platform synthesizing learner's activity data from learning and physical activity contexts. This study focuses on the initial cohort analysis of the students' behavior based on the fine grain interaction data collected in the different systems using visual analytics techniques. Such a data-rich narrative of self-directed in-semester activities is not discussed yet in the literature to our knowledge. We discuss the implications of the trends that is found in our collected dataset for designing AI-support for self-direction skills with the GOAL platform and the scope of deeper analysis to further understand the process.

In this paper, we aim to explore students' study approaches (e.g., deep, strategic, surface) from the logs collected by an electronic textbook (eBook) system. Data was collected from 89 students related to their reading activities both in and out of the class in a Freshman English course. Students are given a task to study reading materials through the eBook system, highlight the text that is related to the main or supporting ideas, and answer the questions prepared for measuring their level of comprehension. Students in and out of class reading times and their usage of the marker feature were used as a proxy to understand their study approaches. We used theory-driven and data-driven approaches together to model the study approaches of students. Our results showed that three groups of students who have different study approaches could be identified. Relationships between students' reading behaviors and their academic performance is also investigated by using association rule mining analysis. Obtained results are discussed in terms of monitoring, feedback, predicting learning outcomes, and identifying problems with the content design.

For the 21st century learner, developing self-direction skill is crucial for both academic activities and maintaining one's healthy lifestyle. While there are technology supports for specific self-regulated learning tasks and health monitoring, research is limited on how to support development of meta-skill of self-direction process itself. In our work, we focus on designing seamless technology infrastructure to foster self-directedness of learners. We consider learning and physical activities data as a context and DAPER (data collection-analyze-plan-execution monitoring-reflect), as a data-driven self-direction skill execution and acquisition model. We bridge Learning Analytics and Quantified-Self approaches to develop the GOAL (Goal Oriented Active Learner) system to support synchronize-visualize-analyze multisource data regarding learners' learning and physical activities. This paper proposes a measurement rubric as a basis of adaptive scaffolding for skill development during the process.

Students' learning behaviors in an online learning environment can be automatically recorded by learning systems. Such learning records provide new opportunities to model students' learning process. On the other hand, it has become more common to see students having wearable devices that assist in tracking their personal physical activities. These activity tracking can be integrated into a data-rich context for training students for developing their data-informed self-direction skills. We are building the GOAL (Goal Oriented Active Learner) system to support the development of self-direction skills using learning and health activity data. A key phase in any self-directed activity is goal setting and planning. This paper will introduce how to build a new model for self-planning and support the acquisition of planning skills in the GOAL system. We combine learners’ data from the self-directed activity and their interaction trace to build the model in the GOAL system. The modeling involves computing of trend value and degree of plan difficulty, then diagnosis of planning skills using a 5-point scoring criteria. An adaptive support is selected based on the computed score. The contribution of this work is modeling planning and promoting planning skills in a data-driven manner. Our approach grounds the theory of self-direction skills and enables learners to develop the skills in everyday life.

In this paper, we analyzed the relationships between students’ engagement and academic performance in the digital textbook system. To measure students’ engagement, we first extracted features from the students’ digital textbook reading logs (click-streams) that represent their engagement with the contents. Then, we used percentile rank transformation to create normalized engagement scores and an overall engagement score. In the analysis, we first investigated the correlation between engagement scores’ and the students’ final scores. Second, we modeled students’ transition patterns from the engagement to academic performance by using Markov Chains. Third, we analyzed engagement patterns of the students with different academic performance levels. Our results showed that there is a positive moderate correlation between students’ academic performance and their engagement with digital textbooks. Our results also revealed that a single engagement score can be used to measure students’ engagement with the system, which is easy to understand by non-expert users. We also introduced our dashboard interventions that are developed based on this engagement score.

In this paper, a model for identifying e-book reading style is proposed and applied onto a learning log dataset. Learning log data available as non-structured data source is processed to identify patterns of reading exhibited by users using three main structures: reading sessions, reads and passages. These structures are used to extract information on users’ reading style to be used as part of user modeling process. The proposed model is applied on a set of log data generated by university students during one semester of digital resource use. The findings show students adopt predominantly receptive reading style, while responsive style occurs rarely. Further analysis revealed no significant relationships between reading style variables and student academic success for the Architecture course indicating the variables of responsive and receptive reading bring new information as part of user modeling.

Learning English as a foreign language is a core part of K-12 education for many countries in which English is not the main spoken language, and especially in Asia. One of the fundamental tasks that students encounter is to learn vocabulary that is a part of the assigned curriculum. These are often sourced from reference materials or assigned vocabulary lists and may not consider the learner’s current proficiency or the semantic context of words that were recently learnt. By suggesting vocabulary that have similar proficiency or semantic contexts to what a student has recently studied could improve and support vocabulary learning. In this paper, we propose a method for recommending words that have similar difficulty and semantic context with previous words learnt based on the analysis of prescribed textbooks for Japanese junior high school students. This research could be used to guide a student learning English by helping them select a sequence of vocabulary that is appropriate.

Learning Analytics (LA) platforms can gather data from the teaching-learning interactions during a course. While there have been previous discussions regarding the individual tools, limited scholarship describes the utility of a LA framework for supporting evidence-based teaching-learning practices. We have proposed LEAF, a framework to bridge that gap. We implement the framework in a platform by integrating LMS, learning behaviour sensors such as an ebook reader, learning analytics dashboard and an evidence portal through Learning Tools Interoperability (LTI). The platform was then made available to teachers from different colleges in India to orchestrate their course offering for one semester. This paper describes the design of the teacher training module for the adoption of the platform based on the A2I2 model as its theoretical basis. The A2I2 model explicitly focuses on encouraging scholarship of learning and teaching among participating teachers and thus is an ideal candidate for utilizing an evidence-based framework.

Current technology enables tracking of various learning and physical activities. User can use the data to analyze issues in the execution of those activities. Current work focuses on this analysis phase of data-informed self-directed activity cycle and proposes a measurement framework of the skill while learners work in a data-rich context. It is a paradigm shift to support and measure analysis skill from previous approaches which mostly rely on questionnaire-based measurements. In our approach, we emphasize the monitoring of learner’s analytical process and the automatic evaluation of the analysis results through system. Based on that, an automated measurement is carried out in the system to depict learner’s analysis skill and changes of skill. Additionally, we elaborate the framework in the context of the GOAL system which provides affordances of analysis based on physical and reading activity data.

MOOCs are increasingly being used to impart education at different levels. However, skill based courses are still in their early age. This paper presents a study done on a 3D animation MOOC. Learner data of performance in quizzes, perception of learning, and interest was gathered. Participants were grouped, accordingly to their prior exposure to online courses, as first time learners experienced learners. The effectiveness (9.1% completion rate) of the MOOC, was analyzed by comparing the perceptions of these two groups. First time learners perceived the contribution of the video towards their learning gain, more than the experienced learners. No difference in the perceived degree of contribution of specific aspects of the course like quizzes, assignments, and discussion forums was found.

Skill development is being promoted throughout India, as a channel to create employable resource. Massive Online Open Courses (MOOCs) can provide a platform to enable such skill development programs at scale. The IITBombayX course, Basic 3D animation using Blender (SKANI101x), was the first of its kind offering of a Skill Development MOOC (sdMOOC) for the Indian learners. Over its two offerings, 6457 participants registered, 2465 (38%) were active and 1132 (19%) were certified. However while producing and conducting the course we realized the lack of content creation and pedagogic strategies for such sdMOOCs. Analysis of our pilot offering of SKANI101x, highlighted its effectiveness for both, first-time and experienced online learners. In the second offering we modified pedagogic strategies to foster student-instructor interactions that resulted in higher engagement and completion rate. This paper discusses the rationale of the decisions and reports an evaluation study of participants’ performance, engagement, and perceptions regarding the components of the course over two offerings. This provides an evidence for the effectiveness of the content creation and pedagogic strategies implemented.

Hastas (Hand Gestures) are key elements in any of the Indian Classical dance forms. They have both functional aspects and an aesthetic appeal during Nritta (pure dance) performances. In this paper we report a pilot study conducted to understand what is the desired teaching-learning experience for teaching the Hastas as perceived by the students and the instructors. Based on these need analysis we propose a design for developing a mobile application "HasTA", as a Bharatanatyam gesture tutor. The design of the various elements in the tutor applies multimedia-learning principles to develop the learner interface. A proposed instructional strategy to use this application has essential 3 phases: demo - practice - perfect. An idea of a Blender Game Engine (BGE) based Demo, Practice and Perfect module in the application by integrating Kinect or web cam is discussed.

We have created a visual representation called Stratified Attribute Tracking (SAT) Diagram to explicate trends that are otherwise implicit in learning analytics data. SAT Diagram is a unified graph that enables tracking individual attribute values in a dataset and stratifying them according to criteria set by the researcher. SAT diagram represents the transition of samples between strata across attributes. In this paper we introduce the SAT diagram and illustrate how to generate, interpret and analyze them. We believe the process of SAT diagram generation would enable exploring deeper research questions on learning data.

Think-Pair-Share (TPS) is a classroom-based active learning strategy, in which students work on a problem posed by the instructor, first individually, then in pairs, and finally as a class-wide discussion. TPS has been recommended for its benefits of allowing students to express their reasoning, reflect on their thinking, and obtain immediate feedback on their understanding. While TPS is intended to promote student engagement, there is a need for research based evidence on the nature of this engagement. In this study, we investigate the quantity and quality of student engagement in a large CS1 class during the implementation of TPS activities. We did classroom observations of students over a period of ten weeks and thirteen TPS activities. We determined patterns of student engagement in the three phases using a real-time classroom observation protocol that we developed and validated. We found that 83% of students on average were fully or mostly engaged. Predominant behaviors displayed were writing the solution to the problem (Think), discussing with neighbor or writing (Pair), and following class discussion (Share). We triangulated results with survey data of student perceptions. We find that students report being highly engaged for 62% during Think phase and 70% during Pair phase.