M. Liu, R.A. Calvo, V. Rus (2014) “Automatic Generation and Ranking of Questions for Critical Review”. Educational Technology & Society. Volume 17, Issue 2, 2014.
Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based system which automatically generates specific trigger questions from citations for critical review support. The performance of the proposed question ranking models was evaluated and the quality of generated questions is reported. Experimental results showed an accuracy of 75.8% on the top 25% ranked questions. These top ranked questions are as useful for self-reflection as questions generated by human tutors and supervisors. A qualitative analysis was also conducted using an information seeking question taxonomy in order to further analyze the questions generated by humans. The analysis revealed that explanation and association questions are the most frequent question types and that the explanation questions are considered the most valuables by student writers.
I am privileged to be part of the LEADS partnership, a research network led by Susanne Lajoie at McGill University. This paperthe first coming from our subproject – with a rather long title “Systematic Evaluation of the Effectiveness of TREs through Software Platform Development for Data Mining across Multiple Disciplines and Tracking Changes in Affective and Cognitive Growths”
J. M. Harley, F. Bouchet, S.Hussain, R. Azevedo, R. Calvo; A Multi-Componential Analysis of Emotions during Complex Learning with an Intelligent Multi-Agent System; AERA2014 Symposium: Interdisciplinary Approaches for Analysing Data from Multiple Affective Channels with Computer-Based Learning Environments.
Abstract. In this paper we discuss the methodology and results of aligning three different emotional measurement methods (automatic facial expression recognition, self-report, electrodermal activation) and their agreement regarding learners’ emotions. Data was collected from 67 undergraduate students from a North American university who interacted with MetaTutor, an intelligent, multi-agent, hypermedia environment for learning about the human circulatory system, for a 1 hour learning session (Azevedo et al., 2013, Harley, Bouchet, & Azevedo, 2013). A webcam was used to capture videos of learners’ facial expressions, which were analyzed using automatic facial recognition software (FaceReader 5.0). Learners’ physiological arousal was measured using Affectiva’s Q-Sensor 2.0 electrodermal activation bracelet. Learners self-reported their experience of 19 different emotional states (including basic, learner-centered, and academic achievement emotions) using the Emotion-Value questionnaire (Harley et al., 2013). They did so on five different occasions during the learning session, which were used as markers to align data from FaceReader and Q-Sensor. We found a high agreement between the facial and self-report data (75.6%) when similar emotions were grouped together along theoretical dimensions and definitions (e.g., anger and frustration) (Harley, et al., 2013). However, our new results examining the agreement between the Q-Sensor and these two methods suggests that electrodermal (EDA/physiological) indices of emotions do not have a tightly coupled (Gross, Sheppes, & Urry, 2011) relationship with them. Explanations for this finding are discussed.
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H. Monkaresi, R. A. Calvo, H. Yan, (To appear) “A Machine Learning Approach to Improve Contactless Heart Rate Monitoring Using a Webcam”, IEEE Journal of Biomedical and Health Informatics (J-BHI)
has been accepted for publication. You can read the complete PrePublication PDF
Unobtrusive, contactless recordings of physiological signals is very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user’s skin. Recent advances in contact-free physiological signals open the door to many new types of applications. This technology promises to measure heart rate (HR) and respiration using video only.
Daniel Johnson, Director of the Games Research and Interaction Lab will discuss when and how videogames have a positive influence on wellbeing. Specifically; the predictors of hours spent playing videogames; the genres, modes of play and experiences during play that influence wellbeing; the predictors of obsessive and harmonious passion for play; the influence of videogame on mood; and the differences in brain activity associated with playing with humans versus AI controlled teammates.
19 November | University of Sydney – View event details.
Download Videogames and Wellbeing: A comprehensive Review a omprehensive review of more than 200 research papers undertaken by the Young and Well CRC’s Gaming Research Group and led by Daniel Johnson.
Director of the Cambridge University Well-being Institute and adviser to the UK Government, Felicia Huppert will present the Sydney Ideas Lecture “Flourishing from Science to Policy” this month.
This presentation will explore the issue of how well-being should be defined and measured, and its principle determinants at the individual and population level. Evidence from behavioral science and neuroscience will be presented, which supports the use of well-being interventions as an effective means of enhancing flourishing in individuals and organizations. Particular attention will be paid tomindfulness training, which with its emphasis on curiosity, awareness and kindness towards oneself and others, can be regarded as foundational to flourishing.
> 19 November | University of Sydney – View event details.