Curious teachers, create curious learners and great historians

© 2018, © 2018 ASPE. Engel, S.[2011. “Children’s Need to Know: Curiosity in Schools.” Harvard Educational Review 81 (4): 625–645] stated that curiosity should be cultivated in our schools as it is intrinsic to children’s development. However, this is often absent from classrooms. In this paper we aim to explore some of the factors that have led to a lack of curiosity in today’s classrooms by identifying the impact of rapid policy and curriculum change. We will then justify the importance of creative teaching to develop curiosity, not only in children but also in their teachers–curious teachers develop curious learners. We will conclude by sharing some case studies to illustrate how curiosity can be developed using history lessons as a platform

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

“It’s like a compass which I use to find direction”: Findings and learning from an evaluation of an App designed to support the teaching of reading comprehension in rural and township schools in South Africa

South Africa has low literacy levels and teachers face multiple challenges in their endeavours to elevate levels of literacy. This is especially prevalent in rural and township schools where teachers face the additional challenges of isolation, limited resources and access to professional development. This article reports on the findings and learning from a preliminary research study which piloted a handheld mobile phone App. This collaborative project, between a university in Kwa-Zulu Natal and one in England, aimed to support in-service and preservice teachers in rural and township settings to use the App to assess and match books to learners’ (aged 9 to 12) stage of reading development in order to facilitate their independent reading and provide teachers with a range of strategies for teaching comprehension that could supplement other professional development available. In-service teachers (n= 120) and preservice teachers (n=93) took part in this mixed methods study. The main finding from the study was that whilst participants were positive about the App, many did not access the App independently. This article discusses the broader issues, including participants foundational knowledge and literacy research participation considerations, that may have underpinned this finding in this collaborative Global North and South research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press