Information System Framework for Training Teachers on Computational Thinking

The call for a holistic integration of computational thinking (CT) skills across all subjects in the newly revised Malaysian curriculum of 2017 has brought to attention an urgent need to prepare teachers with the new syllabus, teaching and learning (TL) tools, and techniques, to promote effective computational thinking knowledge dissemination in their daily classroom practices. However, a preliminary investigation revealed that many teachers were still unaware of the changes within the new curriculum. There was an apparent lack of understanding of computational thinking skills in general. The study intends to propose a conceptual framework to develop knowledge about computational thinking skills among Malaysian teachers, to enhance their pedagogical repertoire to include elements of computational thinking into their teaching content. The study employs mixed-method research to capture data and construct interventions. The Information Systems Design Theory (ISDT) is used to design an effective information system (IS) and set a plan for developing the conceptual framework

Computational thinking for teachers: Development of a localised E-Learning System

Malaysia has introduced computational thinking skills as part of a curriculum integration update to meet the global trends in 21st-century education, focusing on empowering digital literacy. Nevertheless, a preliminary investigation revealed an apparent lack of understanding of computational thinking skills in general among teachers. The study explores the feasibility of developing a localized E-learning system to train computational thinking skills among teachers. An E-learning system, termed as myCTGWBL, was developed on the basis of a newly proposed conceptual framework to present computational thinking teaching-learning repertoire to the teachers. The hypothesis is that myCTGWBL would develop teachers' computational thinking and its position in teaching–learning understanding. myCTGWBL relevance was tested through DeLone and McLean's information system and Urbach's collaboration quality construct. To determine the success factors, partial least squares structural equation modeling was used. A total of 369 teachers participated in a two-stage survey. Participants' understanding of computational thinking and perceptions were recorded at the pre- and post-intervention phases. Open-ended questions of the surveys were analyzed using a simple text analysis technique. The closed-ended questions surveys were analyzed using SPSS Statistics 22.0. A significant improvement in teachers' computational thinking teaching–learning repertoire in a relatively short period has been recorded. Teachers also demonstrated increased confidence in the future delivering computational thinking-based lessons. The E-learning conceptual framework has illustrated the predictive power between user intent, user satisfaction, and Computational thinking (CT) knowledge benefits. Results demonstrate that myCTGWBL could be used to guide future planning when establishing CT knowledge acquisition initiatives, particularly among teachers

Preliminary Investigation: Teachers’ Perception on Computational Thinking Concepts

As Computational Thinking (CT) is to be integrated into Malaysian syllabus by the year of 2017, this study therefore is designed to explore Malaysian teachers’ perception on CT. A survey method is employed; questions were constructed based on the Technology Acceptance Model (TAM) to acquire teachers’ perception on CT. 159 teachers from all over Malaysia completed the survey form. Spearman’s Rank Order correlation was implemented on the obtained data. This study managed to present teachers perception on CT via perceived usefulness of CT, perceived ease of CT integration into teaching and learning practices, teachers’ attitude towards CT and their intention to integrate CT into their classroom, their basic understanding on CT and their concern on CT integration. Our investigation shows teachers had a weak understanding of CT, which led to unnecessary concerns related to the CT integration. The results also show strong positive correlation on perceived ease of CT integration with behavioral intention and teachers’ attitude with behavioral intention

ROBBIE: Robust Bias Evaluation of Large Generative Language Models

As generative large language models (LLMs) grow more performant and prevalent, we must develop comprehensive enough tools to measure and improve their fairness. Different prompt-based datasets can be used to measure social bias across multiple text domains and demographic axes, meaning that testing LLMs on more datasets can potentially help us characterize their biases more fully, and better ensure equal and equitable treatment of marginalized demographic groups. In this work, our focus is two-fold: (1) Benchmarking: a comparison of 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative LLMs. Out of those 6 metrics, AdvPromptSet and HolisticBiasR are novel datasets proposed in the paper. The comparison of those benchmarks gives us insights about the bias and toxicity of the compared models. Therefore, we explore the frequency of demographic terms in common LLM pre-training corpora and how this may relate to model biases. (2) Mitigation: we conduct a comprehensive study of how well 3 bias/toxicity mitigation techniques perform across our suite of measurements. ROBBIE aims to provide insights for practitioners while deploying a model, emphasizing the need to not only measure potential harms, but also understand how they arise by characterizing the data, mitigate harms once found, and balance any trade-offs. We open-source our analysis code in hopes of encouraging broader measurements of bias in future LLMs.Comment: EMNLP 202

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

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

Senior Legacy 2019: Jane Ung

To challenge myself and others on the journey of self-discovery so that, together, our unique strengths can change the world.https://griffinshare.fontbonne.edu/senior-legacy-2019/1009/thumbnail.jp

Comparison of Cisplatin and Nitrogen Mustard Derivatives in Cancer Treatment

Platinum-containing complexes and nitrogen mustard derivatives are two drug families for cancer treatment. The two core drugs of the families were Cisplatin, created in 1845, and Chlormethine (mustine), created in 1925. Cisplatin is used to treat cancers like ovarian, breast, lung, esophageal, and many more. Nitrogen mustards are used as palliative care in lung and breast cancers as well as treatment for Hodgkin’s disease. The two families of drugs in treating cancer will be compared in terms of mechanism, side effects, and resistance factors. The report will also focus on the importance of the Pt metal in cancer treatment

Comparison of Cisplatin and Nitrogen Mustard Derivatives in Cancer Treatment

Platinum-containing complexes and nitrogen mustard derivatives are two drug families for cancer treatment. The two core drugs of the families were Cisplatin, created in 1845, and Chlormethine (mustine), created in 1925. Cisplatin is used to treat cancers like ovarian, breast, lung, esophageal, and many more. Nitrogen mustards are used as palliative care in lung and breast cancers as well as treatment for Hodgkin’s disease. The two families of drugs in treating cancer will be compared in terms of mechanism, side effects, and resistance factors. The report will also focus on the importance of the Pt metal in cancer treatment