‘‘There’s so much more to it than what I initially thought’’: Stepping into researchers’ shoes with a class activity in a first year psychology survey course

In psychology, it is widely agreed that research methods, although central to the discipline, are particularly challenging to learn and teach, particularly at introductory level. This pilot study explored the potential of embedding a student-conducted research activity in a one-semester undergraduate Introduction to Psychology survey course, with the aims of (a) engaging students with the topic of research methods; (b) developing students’ comprehension and application of research methods concepts; and (c) building students’ ability to link research with theory. The research activity explored shoe ownership, examining gender differences and relationships with age, and linking to theories of gender difference and of consumer identity. The process of carrying out the research and reflecting on it created a contextualized, active learning environment in which students themselves raised many issues that research methods lectures seek to cover. Students also wrote richer assignments than standard first year mid-term essay

Deep reinforcement active learning for human-in-the-loop person re-identification

Most existing person re-identification(Re-ID) approaches achieve superior results based on the assumption that a large amount of pre-labelled data is usually available and can be put into training phrase all at once. However, this assumption is not applicable to most real-world deployment of the Re-ID task. In this work, we propose an alternative reinforcement learning based human-in-the-loop model which releases the restriction of pre-labelling and keeps model upgrading with progressively collected data. The goal is to minimize human annotation efforts while maximizing Re-ID performance. It works in an iteratively updating framework by refining the RL policy and CNN parameters alternately. In particular, we formulate a Deep Reinforcement Active Learning (DRAL) method to guide an agent (a model in a reinforcement learning process) in selecting training samples on-the-fly by a human user/annotator. The reinforcement learning reward is the uncertainty value of each human selected sample. A binary feedback (positive or negative) labelled by the human annotator is used to select the samples of which are used to fine-tune a pre-trained CNN Re-ID model. Extensive experiments demonstrate the superiority of our DRAL method for deep reinforcement learning based human-in-the-loop person Re-ID when compared to existing unsupervised and transfer learning models as well as active learning models

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning

ELF: The electronic learning facilitator

As the world‐wide computer network becomes ubiquitous, new tools have been developed, such as the World Wide Web (WWW), for the delivery of multimedia hypertext‐based documents. Similarly, there has been an explosion in the amount of email, bulletin boards, and Usenet News available. This has led to a major problem of information overload: we are slowly but surely being overwhelmed by the amount of information available to us