The ability for a teacher to engage all students in active learning processes in classroom constitutes a crucial prerequisite for enhancing students achievement. Teachers' attentional processes provide important insights into teachers' ability to focus their attention on relevant information in the complexity of classroom interaction and distribute their attention across students in order to recognize the relevant needs for learning. In this context, mobile eye tracking is an innovative approach within teaching effectiveness research to capture teachers' attentional processes while teaching. However, analyzing mobile eye-tracking data by hand is time consuming and still limited. In this paper, we introduce a new approach to enhance the impact of mobile eye tracking by connecting it with computer vision. In mobile eye tracking videos from an educational study using a standardized small group situation, we apply a state-ofthe-art face detector, create face tracklets, and introduce a novel method to cluster faces into the number of identity. Subsequently, teachers' attentional focus is calculated per student during a teaching unit by associating eye tracking fixations and face tracklets. To the best of our knowledge, this is the first work to combine computer vision and mobile eye tracking to model teachers' attention while instructing.
The first study which links MET and high-inference assessment has been done by @cite_9 . They used fixation points and manually assigned them to a list of eight standard area of interests (e.g. black board, instructional material, student material, etc.). They investigated the variation of different skills and variables among expert and novice teachers.
@cite_9
1981869448 2275056699 1518484552 36556894
Classroom observation research and research on teacher expertise are similar in their reliance on observational data with high-inference procedure to assess the quality of instruction. Expertise research usually uses low-inference measures like eye tracking to identify qualitative difference between expert and novice behaviors and cognition. In this study, we used mobile eye-tracking technology to create a low inference quality indicator for the comparison of experienced and student teachers. The distribution of visual fixations on students was measured using Gini coefficients based on the observation of van den Bogert, van Bruggen, Kostons, and Jochems (Teacher and Teacher Education, 37, 208–216, 2014) that expert teachers show better classroom monitoring. Results confirm that student teachers have a higher Gini coefficient than experienced teachers indicating weaker classroom monitoring. However, the Gini coefficient did not correlate in the predicted way with trained observer coding of video footage of the same classrooms using the Classroom Assessment Scoring System (CLASS) (Pianta, Hamre, Haynes, Mintz, & La Paro, 2007) although the mean differences in behavioral management were higher for the experienced teachers as expected. The CLASS dimension Quality of Feedback was significantly related to the Gini coefficient as an interaction with expertise: Only for novice teachers that a high quality of feedback was negatively associated with monitoring of the classroom. Texts with potential educational value are becoming available through the Internet (e.g., Wikipedia, news services). However, using these new tests in classrooms introduces many challenges, one of which is that they usually lack practice exercises and assessments. Here, we address part of this challenge by automating the creation of specific type of assessment item. Specifically, we focus automatically generating factual WH questions. Our goal is to create an automated system that can take as input a text and produce as output questions for assessing a reader's knowledge of the information in the text. The questions could then be presented to a teacher, who could select and revise the ones that he or she judges to be useful. After introducing the problem, we describe some of the computational and linguistic challenges presented by factual question generation. We then present an implemented system that leverages existing natural language processing techniques to address some of these challenges. The system uses a combination or manually encoded transformation rules and a statistical question ranker trained on a tailored dataset of labeled system output. We present experiments that evaluate individual components of the system as well as the system as a whole. We found, among other things, that the question ranker roughly doubled the acceptability rate of top-ranked questions. In a user study, we tested whether K-12 teachers could efficiently create factual questions by selecting and revising suggestions from the system. Offering automatic suggestions reduced the time and effort spent by participants, though it also affected the types or questions that were created. This research supports the idea that natural language processing can help teachers efficiently create instructional content. It provides solutions to some of the major challenges in question generation and an analysis and better understanding of those that remain. This paper considers the problem of knowledge inference on large-scale imperfect repositories with incomplete coverage by means of embedding entities and relations at the first attempt. We propose IIKE (Imperfect and Incomplete Knowledge Embedding), a probabilistic model which measures the probability of each belief, i.e. @math , in large-scale knowledge bases such as NELL and Freebase, and our objective is to learn a better low-dimensional vector representation for each entity ( @math and @math ) and relation ( @math ) in the process of minimizing the loss of fitting the corresponding confidence given by machine learning (NELL) or crowdsouring (Freebase), so that we can use @math to assess the plausibility of a belief when conducting inference. We use subsets of those inexact knowledge bases to train our model and test the performances of link prediction and triplet classification on ground truth beliefs, respectively. The results of extensive experiments show that IIKE achieves significant improvement compared with the baseline and state-of-the-art approaches. To date, few attempts have been made to develop new methods and validate existing ones for automatic evaluation of discourse coherence in the noisy domain of learner texts. We present the first systematic analysis of several methods for assessing coherence under the framework of automated assessment (AA) of learner free-text responses. We examine the predictive power of different coherence models by measuring the effect on performance when combined with an AA system that achieves competitive results, but does not use discourse coherence features, which are also strong indicators of a learner's level of attainment. Additionally, we identify new techniques that outperform previously developed ones and improve on the best published result for AA on a publically-available dataset of English learner free-text examination scripts.