Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

A review of the research literature relating to ICT and attainment

Summary of the main report, which examined current research and evidence for the impact of ICT on pupil attainment and learning in school settings and the strengths and limitations of the methodologies used in the research literature

Review of research and evaluation on improving adult literacy and numeracy skills

The purposes of this literature review are threefold. First, this review summarises findings of the research from the last decade in six fields identified by the Department for Business, Innovation and Skills (BIS) as critical to its forward planning: (1) the economic, personal and social returns to learning; (2) the quality and effectiveness of provision; (3) the number of learning hours needed for skills gain; (4) learner persistence; (5) the retention and loss of skills over time; (6) the literacy and numeracy skills that are needed. Second, this review assesses this evidence base in terms of its quality and robustness, identifying gaps and recommending ways in which the evidence base can be extended and improved. Thirdly, this review attempts to interpret the evidence base to suggest, where possible, how returns to ALN learning for individuals, employers and wider society might be increased through effective and cost-effective interventions

Homo economicus in visual search

How do reward outcomes affect early visual performance? Previous studies found a suboptimal influence, but they ignored the non-linearity in how subjects perceived the reward outcomes. In contrast, we find that when the non-linearity is accounted for, humans behave optimally and maximize expected reward. Our subjects were asked to detect the presence of a familiar target object in a cluttered scene. They were rewarded according to their performance. We systematically varied the target frequency and the reward/penalty policy for detecting/missing the targets. We find that 1) decreasing the target frequency will decrease the detection rates, in accordance with the literature. 2) Contrary to previous studies, increasing the target detection rewards will compensate for target rarity and restore detection performance. 3) A quantitative model based on reward maximization accurately predicts human detection behavior in all target frequency and reward conditions; thus, reward schemes can be designed to obtain desired detection rates for rare targets. 4) Subjects quickly learn the optimal decision strategy; we propose a neurally plausible model that exhibits the same properties. Potential applications include designing reward schemes to improve detection of life-critical, rare targets (e.g., cancers in medical images)