Advancing digital healthcare research and machine learning-aided knowledge extraction amidst the COVID-19 pandemic

The rapid progression of biomedical research coupled with the explosion of scientific literature has generated an exigent need for efficient and reliable systems of knowledge extraction. This dissertation contends with this challenge through a concentrated investigation of digital health, Artificial Intelligence, and specifically Machine Learning and Natural Language Processing's (NLP) potential to expedite systematic literature reviews and refine the knowledge extraction process. The surge of COVID-19 complicated the efforts of scientists, policymakers, and medical professionals in identifying pertinent articles and assessing their scientific validity. This thesis presents a substantial solution in the form of the COKE Project, an initiative that interlaces machine reading with the rigorous protocols of Evidence-Based Medicine to streamline knowledge extraction. In the framework of the COKE (“COVID-19 Knowledge Extraction framework for next-generation discovery science”) Project, this thesis aims to underscore the capacity of machine reading to create knowledge graphs from scientific texts. The project is remarkable for its innovative use of NLP techniques such as a BERT + bi-LSTM language model. This combination is employed to detect and categorize elements within medical abstracts, thereby enhancing the systematic literature review process. The COKE project's outcomes show that NLP, when used in a judiciously structured manner, can significantly reduce the time and effort required to produce medical guidelines. These findings are particularly salient during times of medical emergency, like the COVID-19 pandemic, when quick and accurate research results are critical.

Network analysis and machine learning assist drug repurposing and safety assessment in neurological diseases

In recent decades, two prominent trends have influenced the data modeling field, namely network analysis and machine learning. This thesis explores the practical applications of these techniques within the domain of drug research, unveiling their multifaceted potential for advancing our comprehension of complex biological systems. The research undertaken during this PhD program is situated at the intersection of network theory, computational methods, and drug research. Across six projects presented herein, there is a gradual increase in model complexity. These projects traverse a diverse range of topics, with a specific emphasis on drug repurposing and safety in the context of neurological diseases. The aim of these projects is to leverage existing biomedical knowledge to develop innovative approaches that bolster drug research. The investigations have produced practical solutions, not only providing insights into the intricacies of biological systems, but also allowing the creation of valuable tools for their analysis. In short, the achievements are: • A novel computational algorithm to identify adverse events specific to fixed-dose drug combinations. • A web application that tracks the clinical drug research response to SARS-CoV-2. • A Python package for differential gene expression analysis and the identification of key regulatory "switch genes". • The identification of pivotal events causing drug-induced impulse control disorders linked to specific medications. • An automated pipeline for discovering potential drug repurposing opportunities. • The creation of a comprehensive knowledge graph and development of a graph machine learning model for predictions. Collectively, these projects illustrate diverse applications of data science and network-based methodologies, highlighting the profound impact they can have in supporting drug research activities.

Evaluation Metrics and Transfer Learning for Sales Prediction

Sales prediction plays a huge role in modern business strategies. One of it's many use cases revolves around estimating the effects of promotions. While promotions generally have a positive effect on sales of the promoted product, they can also have a negative effect on those of other products. This phenomenon is calles sales cannibalisation. Sales cannibalisation can pose a big problem to sales forcasting algorithms. A lot of times, these algorithms focus on sales over time of a single product in a single store (a couple). This research focusses on using knowledge of a product across multiple different stores. To achieve this, we applied transfer learning on a neural model developed by Kantar Consulting to demo an approach to estimating the effect of cannibalisation. Our results show a performance increase of between 10 and 14 percent. This is a very good and desired result, and Kantar will use the approach when integrating this test method into their actual systems.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.

Exploring Relationship between Pakistani Teachers’ Pedagogical Beliefs and ICT Use for Teaching and Learning

Background: The aim of present study is to investigate relationship between Pakistani teachers’ pedagogical beliefs and ICT use for teaching and learning. Previous studies found close relationship between pedagogical beliefs and teaching practices including use of ICT. However, variation in results is also reported and attributed to cultural and contextual differences. Methodology: Quantitative approach was used to study a sample of 231 Pakistani basic education teachers from middle and upper-middle range private schools, mostly from large urban centres. Results: This study confirmed previously study results that constructivist-compatible pedagogical beliefs are significantly related to both traditional and constructivist use of ICT. However, it is also found that traditional-transmission pedagogical beliefs are significantly related to traditional use of ICT – a finding not reported in previous studies. Some amounts of ICT training for pedagogical purpose exhibit significant impact on ICT use, in particular constructivist use of ICT. Similarly age also play a role as younger teachers are more likely to use ICT, however, no significant difference of pedagogical beliefs and ICT use was found between male and female teachers. Recommendation for practice and further investigation are made in chapter 5.

Shop-floor narratives : how six workers and a researcher describe learning activities

I am a part-time graduate student who works in industry. This study is my narrative about how six workers and I describe shop-floor learning activities, that is learning activities that occur where work is done, outside a classroom. Because this study is narrative inquiry, you wilileam about me, the narrator, more than you would in a more conventional study. This is a common approach in narrative inquiry and it is important because my intentions shape the way that I tell these six workers' stories. I developed a typology of learning activities by synthesizing various theoretical frameworks. This typology categorizes shop-floor learning activities into five types: onthe- job training, participative learning, educational advertising, incidental learning, and self-directed learning. Although learning can occur in each of these activities in isolation, it is often comprised of a mixture of these activities. The literature review contains a number of cases that have been developed from situations described in the literature. These cases are here to make the similarities and differences between the types of learning activities that they represent more understandable to the reader and to ground the typology in practice as well as in theory. The findings are presented as reader's theatre, a dramatic presentation of these workers' narratives. The workers tell us that learning involves "being shown," and if this is not done properly they "learn the hard way." I found that many of their best case lean1ing activities involved on-the-job training, participative learning, incidentalleaming, and self-directed learning. Worst case examples were typically lacking in properly designed and delivered participative learning activities and to a lesser degree lacking carefully planned and delivered on-the-job training activities. Included are two reflective chapters that describe two cases: Learning "Engels" (English), and Learning to Write. In these chapters you will read about how I came to see that my own shop-floor learning-learning to write this thesis-could be enhanced through participative learning activities. I came to see my thesis supervisor as not only my instructor who directed and judged my learning activities, but also as a more experienced researcher who was there to participate in this process with me and to help me begin to enter the research community. Shop-floor learning involves learners and educators participating in multistranded learning activities, which require an organizational factor of careful planning and delivery. As with learning activities, which can be multi-stranded, so too, there can be multiple orientations to learning on the shop floor. In our stories, you will see that these six workers and I didn't exhibit just one orientation to learning in our stories. Our stories demonstrate that we could be behaviorist and cognitivist and humanist and social learners and constructivist in our orientation to learning. Our stories show that learning is complex and involves multiple strands, orientations, and factors. Our stories show that learning narratives capture the essence of learning-the learners, the educators, the learning activities, the organizational factors, and the learning orientations. Learning narratives can help learners and educators make sense of shop-floor learning.

Working Together: Librarian and Student Collaboration Through Active Learning in a Library Eclassroom

Active learning strategies based on several learning theories were incorporated during instruction sessions for second year Biological Sciences students. The instructional strategies described in this paper are based primarily on sociocultural and collaborative learning theory, with the goal being to expand the relatively small body of literature currently available that discusses the application of these learning theories to library instruction. The learning strategies employed successfully involved students in the learning process ensuring that the experiences were appropriate and effective. The researchers found that, as a result of these strategies (e.g. teaching moments based on the emerging needs of students) students’ interest in learning information literacy was increased and students interacted with information given to them as well as with their peers. Collaboration between the Librarians, Co-op Student and Senior Lab Instructor helped to enhance the learning experience for students and also revealed new aspects of the active learning experiences. The primary learning objective, which was to increase the students’ information skills in the Biological Sciences, was realized. The advantages of active learning were realized by both instructors and students. Advantages for students attained during these sessions include having their diverse learning styles addressed; increased interaction with and retention of information; increased responsibility for their own learning; the opportunity to value not only the instructors, but also themselves and their peers as sources of authority and knowledge; improved problem solving abilities; increased interest and opportunities for critical thinking, as a result of the actively exchanging information in a group. The primary advantage enjoyed by the instructors was the opportunity to collaborate with colleagues to reduce the preparation required to create effective library instruction sessions. Opportunities for further research were also discovered, including the degree to which “social loafing” plays a role in collaborative, active learning.

Content and English Language Integrated Learning: The use of Moodle in Aircraft Maintenance Training Programs

The aim of this work is to develop a prototype of an e-learning environment that can foster Content and Language Integrated Learning (CLIL) for students enrolled in an aircraft maintenance training program, which allows them to obtain a license valid in all EU member states. Background research is conducted to retrace the evolution of the field of educational technology, analyzing different learning theories – behaviorism, cognitivism, and (socio-)constructivism – and reflecting on how technology and its use in educational contexts has changed over time. Particular attention is given to technologies that have been used and proved effective in Computer Assisted Language Learning (CALL). Based on the background research and on students’ learning objectives, i.e. learning highly specialized contents and aeronautical technical English, a bilingual approach is chosen, three main tools are identified – a hypertextbook, an exercise creation activity, and a discussion forum – and the learning management system Moodle is chosen as delivery medium. The hypertextbook is based on the technical textbook written in English students already use. In order to foster text comprehension, the hypertextbook is enriched by hyperlinks and tooltips. Hyperlinks redirect students to webpages containing additional information both in English and in Italian, while tooltips show Italian equivalents of English technical terms. The exercise creation activity and the discussion forum foster interaction and collaboration among students, according to socio-constructivist principles. In the exercise creation activity, students collaboratively create a workbook, which allow them to deeply analyze and master the contents of the hypertextbook and at the same time create a learning tool that can help them, as well as future students, to enhance learning. In the discussion forum students can discuss their individual issues, content-related, English-related or e-learning environment-related, helping one other and offering instructors suggestions on how to improve both the hypertextbook and the workbook based on their needs.

Content-based VLE designs improve learning efficiency in constructivist statistics education

Background: We introduced a series of computer-supported workshops in our undergraduate statistics courses, in the hope that it would help students to gain a deeper understanding of statistical concepts. This raised questions about the appropriate design of the Virtual Learning Environment (VLE) in which such an approach had to be implemented. Therefore, we investigated two competing software design models for VLEs. In the first system, all learning features were a function of the classical VLE. The second system was designed from the perspective that learning features should be a function of the course's core content (statistical analyses), which required us to develop a specific-purpose Statistical Learning Environment (SLE) based on Reproducible Computing and newly developed Peer Review (PR) technology. Objectives: The main research question is whether the second VLE design improved learning efficiency as compared to the standard type of VLE design that is commonly used in education. As a secondary objective we provide empirical evidence about the usefulness of PR as a constructivist learning activity which supports non-rote learning. Finally, this paper illustrates that it is possible to introduce a constructivist learning approach in large student populations, based on adequately designed educational technology, without subsuming educational content to technological convenience. Methods: Both VLE systems were tested within a two-year quasi-experiment based on a Reliable Nonequivalent Group Design. This approach allowed us to draw valid conclusions about the treatment effect of the changed VLE design, even though the systems were implemented in successive years. The methodological aspects about the experiment's internal validity are explained extensively. Results: The effect of the design change is shown to have substantially increased the efficiency of constructivist, computer-assisted learning activities for all cohorts of the student population under investigation. The findings demonstrate that a content-based design outperforms the traditional VLE-based design. © 2011 Wessa et al.

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment

Background - The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives - The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods - We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results - The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

A comparison of the effects of social constructivist and traditional approaches to teaching on students' attitude and achievement in high school chemistry

Current views of the nature of knowledge and of learning suggest that instructional approaches in science education pay closer attention to how students learn rather than on teaching. This study examined the use of approaches to teaching science based on two contrasting perspectives in learning, social constructivist and traditional, and the effects they have on students' attitudes and achievement. Four categories of attitudes were measured using the Upper Secondary Attitude Questionnaire: Attitude towards school, towards the importance of science, towards science as a career, and towards science as a subject in school. Achievement was measured by average class grades and also with a researcher/teacher constructed 30-item test that involved three sub-scales of items based on knowledge, and applications involving near-transfer and far-transfer of concepts. The sample consisted of 202 students in nine intact classrooms in chemistry at a large high school in Miami, Florida, and involved two teachers. Results were analyzed using a two-way analysis of covariance (ANCOVA) with a pretest in attitude as the covariate for attitudes and prior achievement as the covariate for achievement. A comparison of the adjusted mean scores was made between the two groups and between females and males. ^ With constructivist-based teaching, students showed more favorable attitude towards science as a subject, obtained significantly higher scores in class achievement, total achievement and achievement on the knowledge sub-scale of the knowledge and application test. Students in the traditional group showed more favorable attitude towards school. Females showed significantly more positive attitude towards the importance of science and obtained significantly higher scores in class achievement. No significant interaction effects were obtained for method of instruction by gender. ^ This study lends some support to the view that constructivist-based approaches to teaching science is a viable alternative to traditional modes of teaching. It is suggested that in science education, more consideration be given to those aspects of classroom teaching that foster closer coordination between social influences and individual learning. ^

HYBRID AND INCREMENTAL FUZZY LEARNING FOR HUMAN SKIN DETECTION

In this paper, a framework for detection of human skin in digital images is proposed. This framework is composed of a training phase and a detection phase. A skin class model is learned during the training phase by processing several training images in a hybrid and incremental fuzzy learning scheme. This scheme combines unsupervised-and supervised-learning: unsupervised, by fuzzy clustering, to obtain clusters of color groups from training images; and supervised to select groups that represent skin color. At the end of the training phase, aggregation operators are used to provide combinations of selected groups into a skin model. In the detection phase, the learned skin model is used to detect human skin in an efficient way. Experimental results show robust and accurate human skin detection performed by the proposed framework.

On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Learning about life and death in early childhood

Inagaki and Hatano (2002) have argued that young children initially understand biological phenomena in terms of vitalism, a mode of construal in which life or life-force is the central causal-explanatory concept. This study investigated the development of vitalistic reasoning in young children's concepts of life, the human body and death. Sixty preschool children between the ages of 3 years, 7 months and 5 years, 11 months participated. All children were initially given structured interviews to assess their knowledge of (1) human body function and (2) death. From this sample 40 children in the Training group were taught about the human body and how it functions to maintain life. The Control group (n = 20) received no training. All 60 children were subsequently reassessed on their knowledge of human body function and death. Results from the initial interviews indicated that young children who spontaneously appealed to vitalistic concepts in reasoning about human body functioning were also more sophisticated in their understanding of death. Results from the posttraining interviews showed that children readily learned to adopt a vitalistic approach to human body functioning, and that this learning coincided with significant development in their understanding of human body function, and of death. The overall pattern of results supports the claim that the acquisition of a vitalistic causal-explanatory framework serves to structure children's concepts and facilitates learning in the domain of biology. (C) 2003 Elsevier Science (USA). All rights reserved.

Learning and Knowledge in the Context of Absorptive Capacity

Develop a new model of Absorptive Capacity taking into account two variables namely Learning and knowledge to explain how companies transform information into knowledge