Eye Tracking and Studying Examples: How Novices and Advanced Learners Study SQL Examples

Eye tracking provides information about a user’s eye gaze movements. For many years, eye tracking has been used in Human Computer Interaction (HCI) research. Similarly, research on computerised educational systems also relies heavily on students’ interactions with systems, and therefore eye tracking has been used to study and improve learning. We have recently conducted several studies on using worked examples in addition to scaffolded problem solving. The goal of the project reported in this paper was to investigate how novices and advanced students learn from examples. The study was performed in the context of SQL-Tutor, a mature Intelligent Tutoring System (ITS) that teaches SQL. We propose a new technique to analyse eye-gaze patterns named EGPA. In order to comprehend an SQL example, students require the information about tables’ names and their attributes which are available in a database schema. Thus, if students paid attention to the database schema, they would understand SQL examples more easily. We analysed students’ eye movement data from different perspectives, and found that advanced students paid more attention to database schema than novices. In future work, we will use the findings from this study to provide proactive feedback or individualised amounts of information

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions

Middle School Teacher Beliefs about Classroom Diversity and their Influence on Differentiated Instructional Practices

Diversity across U.S. classrooms is on the rise which is leading to renewed calls for teachers to meet individual learning needs. Studies indicate the failure to address individual learning needs can lead to higher rates of student disengagement, off-task behaviors, and diminished learning outcomes. Differentiated instruction is an approach to teaching that meets the growing diversity of individual learning needs by considering students\u27 readiness, interest, and learning styles. Differentiated instructional approaches help teachers meet individual learning needs by allowing them to modify instruction as needed. However, despite the apparent benefits of differentiated instruction, teachers are hesitant to abandon other educational models. Research has shown beliefs about student learning influence teachers classroom practices. If teachers do not possess beliefs supporting differentiated classroom practices, then calls for additional implementation may go unheeded. Thus it is important to examine teacher beliefs that may help or hinder implementation of differentiated instructional practices. The purpose of this study was to explore teacher beliefs connected to teaching, learning, and differentiated instruction in diverse classrooms. This study used qualitative case study methodologies to interview, survey, and observe the beliefs and practices of four white, female, veteran middle level educators operating in diverse classroom settings. Data analysis revealed the following themes: (a) differentiated instruction is considered essential, (b) teachers\u27 diversity definitions influenced their differentiated instructional strategies, (c) the classroom environment influences teachers self-efficacy, and (d) professional development sessions support differentiated instructional practices. Conclusions drawn from this study may be used to help improve teacher practices--and ultimately learner outcomes--by informing teacher preparation and professional development, state and local educational policies, and curricular reform efforts

Eye Tracking to Support eLearning

Online eLearning environments to support student learning are of growing importance. Students are increasingly turning to online resources for education; sometimes in place of face-to-face tuition. Online eLearning extends teaching and learning from the classroom to a wider audience with different needs, backgrounds, and motivations. The one-size-fits-all approach predominately used is not effective for catering to the needs of all students. An area of the increasing diversity is the linguistic background of readers. More students are reading in their non-native language. It has previously been established that first English language (L1) students read differently to second English language (L2) students. One way of analysing this difference is by tracking the eyes of readers, which is an effective way of investigating the reading process. In this thesis we investigate the question of whether eye tracking can be used to make learning via reading more effective in eLearning environments. This question is approached from two directions; first by investigating how eye tracking can be used to adapt to individual student’s understanding and perceptions of text. The second approach is analysing a cohort’s reading behaviour to provide information to the author of the text and any related comprehension questions regarding their suitability and difficulty. To investigate these questions, two user studies were carried out to collect eye gaze data from both L1 and L2 readers. The first user study focussed on how different presentation methods of text and related questions affected not only comprehension performance but also reading behaviour and student perceptions of performance. The data from this study was used to make predictions of reading comprehension that can be used to make eLearning environments adaptive, in addition to providing implicit feedback about the difficulty of text and questions. In the second study we investigate the effects of text readability and conceptual difficulty on eye gaze, prediction of reading comprehension, and perceptions. This study showed that readability affected the eye gaze of L1 readers and conceptual difficulty affected the eye gaze of L2 readers. The prediction accuracy of comprehension was consequently increased for the L1 group by increased difficulty in readability, whereas increased difficulty in conceptual level corresponded to increased accuracy for the L2 group. Analysis of participants’ perceptions of complexity revealed that readability and conceptual difficulty interact making the two variables hard for the reader to disentangle. Further analysis of participants’ eye gaze revealed that both the predefined and perceived text complexity affected eye gaze. We therefore propose using eye gaze measures to provide feedback about the implicit reading difficulty of texts read. The results from both studies indicate that there is enormous potential in using eye tracking to make learning via reading more effective in eLearning environments. We conclude with a discussion of how these findings can be applied to improve reading within eLearning environments. We propose an adaptive eLearning architecture that dynamically presents text to students and provides information to authors to improve the quality of texts and questions