Do your eyes give it away? Using eye tracking data to understand students' attitudes towards open student model representations

There is sufficient evidence to show that allowing students to see their own student model is an effective learning and metacognitive strategy. Different tutors have different representations of these open student models, all varying in complexity and detail. EER-Tutor has a number of open student model representations available to the student at any particular time. These include skill meters, kiviat graphs, tag clouds, concept hierarchies, concept lists, and treemaps. Finding out which representation best helps the student at their level of expertise is a difficult task. Do they really understand the representation they are looking at? This paper looks at a novel way of using eye gaze tracking data to see if such data provides us with any clues as to how students use these representations and if they understand them

Exploring Feedback and Gamification in a Data Modeling Learning Tool

Data modeling is an essential part of IT studies. Learning how to design and structure a database is important when storing data in a relational database and is common practice in the IT industry. Most students need much practice and tutoring to master the skill of data modeling and database design. When a student is in a learning process, feedback is important. As class sizes grow and teaching is no longer campus based only, providing feedback to each individual student may be difficult. Our study proposes a tool to use when introducing database modeling to students. We have developed a web-based tool named LearnER to teach basic data modeling skills, in a collaborative project between the University of South-Eastern Norway (USN) and Kristiania University College (KUC). The tool has been used in six different courses over a period of four academic years. In LearnER, the student solves modeling assignments with different levels of difficulty. When they are done, or they need help, they receive automated feedback including visual cues. To increase the motivation for solving many assignments, LearnER also includes gamifying elements. Each assignment has a maximum score. When students ask for help, points are deducted from the score. When students manage to solve many assignments with little help, they may end up at a leaderboard. This paper tries to summarize how the students use and experience LearnER. We look to see if the students find the exercises interesting, useful and of reasonable difficulty. Further, we investigate if the automated feedback is valuable, and if the gamifying elements contribute to their learning. As we have made additions and refinements to LearnER over several years, we also compare student responses on surveys and interviews during these years. In addition, we analyze usage data extracted from the application to learn more about student activity. The results are promising. We find that student activity increases in newer versions of LearnER. Most students report that the received feedback helps them to correct mistakes when solving modeling assignments. The gamifying elements are also well received. Based on LearnER usage data, we find and describe typical errors the students do and what types of assignments they prefer to solve.publishedVersio

Learners' self-assessment and metacognition when using an open learner model with drill down

Metacognition is ‘thinking on thinking’. It is important to educational practices for learners/teachers, and in activities such as formative-assessment and self-directed learning. The ability to perform metacognition is not innate and requires fostering, and self-assessment contributes to this. Literature suggests proven practices for promoting metacognitive opportunities and ongoing enquiry about how technology best supports these. This thesis considers an open learner model (OLM) with a drill-down approach as a method to investigate support for metacognition and self-assessment. Measuring aspects of metacognition without unduly influencing it is challenging. Direct measures (e.g. learners ‘thinking-aloud’) could distort/disrupt/encourage/effect metacognition. The thesis develops methods to evaluate aspects of metacognition without directly affecting it, relevant to future learning-analytics research/OLM design. It proposes a technology specification/implementation for supporting metacognition research and highlights the relevance of using a drill-down approach. Using measures that correspond to post-hoc learner accounts, this thesis identifies a baseline of student activity that is consistent with important regulation of cognition tasks and students’ specific interest in problems. Whilst this does not always influence self-assessment accuracy, students indicating their self-assessment ability can be used as a proxy measure to identify those who will improve. Evidence supports claims that OLMs remain relevant in metacognition research

Multimedia Development of English Vocabulary Learning in Primary School

In this paper, we describe a prototype of web-based intelligent handwriting education system for autonomous learning of Bengali characters. Bengali language is used by more than 211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the population does not have the chance to go to school. This research project was aimed to develop an intelligent Bengali handwriting education system. As an intelligent tutor, the system can automatically check the handwriting errors, such as stroke production errors, stroke sequence errors, stroke relationship errors and immediately provide a feedback to the students to correct themselves. Our proposed system can be accessed from smartphone or iPhone that allows students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a multi-stroke input characters with extremely long cursive shaped where it has stroke order variability and stroke direction variability. Due to this structural limitation, recognition speed is a crucial issue to apply traditional online handwriting recognition algorithm for Bengali language learning. In this work, we have adopted hierarchical recognition approach to improve the recognition speed that makes our system adaptable for web-based language learning. We applied writing speed free recognition methodology together with hierarchical recognition algorithm. It ensured the learning of all aged population, especially for children and older national. The experimental results showed that our proposed hierarchical recognition algorithm can provide higher accuracy than traditional multi-stroke recognition algorithm with more writing variability