A pedagogical design pattern framework:for sharing experiences and enhancing communities of practice within online and blended learning

”Design patterns” were originally proposed in architecture and later in software engineering as a methodology to sketch and share solutions to recurring design problems. In recent years ”pedagogical design patterns” have been introduced as a way to sketch and share good practices in teaching and learning; specifically in the context of technology-enhanced learning (e-learning). Several attempts have been made to establish a framework for describing and sharing such e-learning patterns, but so far they have had limited success. At a series of workshops in a competence-development project for teachers at the University of Copenhagen a new and simpler pedagogical design pattern framework was developed for interfaculty sharing of experiences and enhancing communities of practice in relation to online and blended learning across the university. In this study, the new pedagogical design pattern framework is applied to describe the learning design in four online and blended learning courses within different academic disciplines: Classical Greek, Biostatistics, Environmental Management in Europe, and Climate Change Impacts, Adaptation and Mitigation. Future perspectives for using the framework for developing new E-learning patterns for online and blended learning courses are discussed

A knowledge-based framework to facilitate E-training implementation

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e de ComputadoresNowadays, there is an evident increase of the custom-made products or solutions demands with the objective to better fits to customer needs and profiles. Aligned with this, research in e-learning domain is focused in developing systems able to dynamically readjust their contents to respond to learners’ profiles demands. On the other hand, there is also an increase of e-learning developers which even not being from pedagogical curricula, as research engineers, needs to prepare e-learning programmes about their prototypes or products developed. This thesis presents a knowledge-based framework with the purpose to support the creation of e-learning materials, which would be easily adapted for an effective generation of custom-made e-learning courses or programmes. It embraces solutions for knowledge management, namely extraction from text & formalization and methodologies for collaborative e-learning courses development, where main objective is to enable multiple organizations to actively participate on its production. This also pursues the challenge of promoting the development of competencies, which would result from an efficient knowledge-transfer from research to industry

The Design and Implementation of Online Radiology Modules Using the ADDIE Process and Rapid Prototyping

Medical schools in the United States have begun the process of changing the teaching methodologies used in the classroom. The traditional, teacher-centered environment is shifting toward a more student-centered, active learning environment. Part of this shift is the integration of online learning to deliver a continuously expanding medical curriculum by moving content learning outside the classroom and creating active learning activities for the classroom. As more medical schools adopt online learning as a supplemental teaching tool, medical education faculty are taking on the role of instructional designers without having any theoretical knowledge on adult learning theory or online learning practices. Schools are developing online learning materials without relying on an instructional design framework to guide the analysis, design, development, implementation, and evaluation of the online curriculum. This can result in developing online materials that do not meet the intended objectives, are designed poorly, or do not incorporate learning principles specific to the way humans use computers to learn. At the Herbert Wertheim College of Medicine, the third year radiology clerkship is a requirement of the curriculum; however, the rotation only lasts two weeks, versus the four to seven weeks provided the other six rotations. Student group sessions led by the radiology clerkship director are limited to four hours in the afternoon, Monday through Friday. This limited time has driven the need to explore alternative solutions for the delivery of the learning material to students. This study seeks to apply an instructional design process, ADDIE, to the development of four e-learning modules for a third year, required, radiology clerkship course using the ADDIE process as a framework and incorporating a rapid prototyping approach. The purpose is to identify how to effectively implement an instructional design methodology, ADDIE, using rapid prototyping when developing supplemental online learning materials for a radiology clerkship course

Reducing the risk of e-mail phishing in the state of Qatar through an effective awareness framework

In recent years, cyber crime has focused intensely on people to bypass existing sophisticated security controls; phishing is one of the most common forms of such attack. This research highlights the problem of e-mail phishing. A lot of previous research demonstrated the danger of phishing and its considerable consequences. Since users behaviour is unpredictable, there is no reliable technological protective solution (e.g. spam filters, anti-viruses) to diminish the risk arising from inappropriate user decisions. Therefore, this research attempts to reduce the risk of e-mail phishing through awareness and education. It underlines the problem of e-mail phishing in the State of Qatar, one of world s fastest developing countries and seeks to provide a solution to enhance people s awareness of e-mail phishing by developing an effective awareness and educational framework. The framework consists of valuable recommendations for the Qatar government, citizens and organisations responsible for ensuring information security along with an educational agenda to train them how to identify and avoid phishing attempts. The educational agenda supports users in making better trust decisions to avoid phishing that could complement any technical solutions. It comprises a collection of training methods: conceptual, embedded, e-learning and learning programmes which include a television show and a learning session with a variety of teaching components such as a game, quizzes, posters, cartoons and a presentation. The components were tested by trial in two Qatari schools and evaluated by experts and a representative sample of Qatari citizens. Furthermore, the research proves the existence and extent of the e-mail phishing problem in Qatar in comparison with the UK where people were found to be less vulnerable and more aware. It was discovered that Qatar is an attractive place for phishers and that a lack of awareness and e-law made Qatar more vulnerable to the phishing. The research identifies the factors which make Qatari citizens susceptible to e-mail phishing attacks such as cultural, country-specific factors, interests and beliefs, religion effect and personal characteristics and this identified the need for enhancing Qatari s level of awareness on phishing threat. Since literature on phishing in Qatar is sparse, empirical and non-empirical studies involved a variety of surveys, interviews and experiments. The research successfully achieved its aim and objectives and is now being considered by the Qatari Government

Strategies for Implementing E-Learning Solutions in Ghana’s Public Universities: A Delphi Study

Thousands of qualified university applicants are denied admission into mainstream public universities in Ghana each year mainly due to lack of physical space on campuses, but e-learning has been identified as a way of increasing admissions. However, there have been no strategies for implementing solutions to e-learning, and so this study was conducted to identify these strategies. The conceptual framework comprised of status quo bias, culture, and resistance to change. A qualitative modified Delphi approach was used for the study with three rounds of surveys. The 11 panelists were administrators and/or professors who had been in their positions for 2 years or more in their universities. After three Delphi rounds, participants agreed on 10 strategies: assess overall needs of a university before e-learning is implemented, set goals for implementing e-learning, involve top management in developing and implementing e-learning, assess specific IT needs in order to implement e-learning, assess actual financing options for e-learning, find different options/strategies for implementing e-learning goals, assign responsibilities to specific personnel or committee to oversee the implementation, involve other stakeholders besides top management in developing and implementing e-learning strategies, provide needed resources for e-learning, and provide e-learning training to relevant stakeholders. University administrators in Ghana may use these strategies to implement e-learning and increase admissions each year. More students having access to higher education can lead to better jobs and better standard of living for graduates and their families and also enable them to contribute to Ghana’s economy

Instructional Designer Perspectives of the Usefulness of an Instructional Design Process When Designing E-Learning

Though the number of instructional design models has increased, the usefulness of an instructional design process (linear or iterative) when making design decisions for e-learning solutions remains uncertain. This basic qualitative study was used to explore the perspectives of corporate instructional designers who were mandated to move from a linear to an iterative instructional design process for developing e-learning. The research questions address their perspectives of the usefulness of an instructional design process when making design decisions for e-learning solutions. Data were collected using semistructured interviews with nine instructional designers. Data were analyzed inductively using in vivo and pattern coding to develop themes related to the conceptual framework of the technological pedagogical content knowledge model. The findings indicated the instructional designers use a linear instructional design process for making e-learning designs decision when time is allotted to conduct an analysis and get buy-in from stakeholders, when the opportunity to work independently exists, and when the content is known and less likely to change. Additionally, the instructional designers use an iterative instructional design process for making e-learning design decision when time is allotted for prototyping and getting buy-in from stakeholders as well as when the content is unknown and more likely to change, and they use this iterative process for approving e-learning design decisions about content, presentation, and technology when there are multiple decision-makers. Positive social change might occur if educational leaders and instructional designers leverage the findings to gain insight into the practical application of instructional design processes when designing e-learning solutions

Integrating e-Learning Modules into Engineering Courses to Develop an Entrepreneurial Mindset in Students

Engineering graduates who will be leaders in today’s rapidly changing environment must possess an entrepreneurial mindset and a variety of professional skills in addition to technical knowledge and skills. An entrepreneurial mindset applies to all aspects of life, beginning with curiosity about our changing world, integrating information from various resources to gain insight, and identifying unexpected opportunities to create value. The Kern Entrepreneurial Engineering Network (KEEN) defines curiosity, connections and creating value as three core components of an entrepreneurial mindset. These 3Cs coupled with associated engineering skills forms KEEN’s entrepreneurial mindset framework. An entrepreneurial mindset enables engineers to develop sound technical solutions that address customer needs, are feasible from a business perspective, and have societal benefit. The Tagliatela College of Engineering at the University of New Haven is working to develop an entrepreneurial mindset in its engineering students through a four-faceted framework based on KEEN’s constructs that includes: 1) developing an entrepreneurial mindset amongst faculty; 2) providing curricular components that develop specific student knowledge and skills; 3) structuring the physical environment to promote entrepreneurial minded learning; and 4) providing opportunities for students to engage in meaningful extra-curricular activities. This paper focuses on the curricular component of this framework. As part of these curricular activities, 18 short, self-paced, e-learning modules will be developed and integrated into courses spanning all four years across all engineering and computer science disciplines. Each module contains readings, short videos and self-assessment exercises. Five of these e-learning modules were developed in fall 2014, four of these five were piloted in the Spring 2015 semester, and all five modules were broadly deployed in the Fall 2015 semester. A flipped classroom instructional model is used to integrate the modules into courses. Content is delivered via a short online module outside the class, and student learning is improved by reinforcing the content covered in the module through class discussions and contextual activities. Direct and indirect assessment is performed through formative and summative class assessments and module specific pre and post surveys, respectively. The five integrated e-learning modules presented in this paper are: 1) Developing customer awareness and quickly testing concepts through customer engagement, 2) Learning from failure, 3) Cost of production and market conditions, 4) Building, sustaining and leading effective teams and establishing performance goals, and 5) Applying systems thinking to solve complex problems. The first two modules were integrated into freshman classes, the third into a sophomore class, the fourth into third year laboratory courses, and the fifth into senior design courses. This paper describes the learning outcomes and the reinforcement activities conducted in the courses into which they were integrated for two of these modules. The findings of the module specific surveys and the assessment results are also presented

Derandomized Novelty Detection with FDR Control via Conformal E-values

Conformal prediction and other randomized model-free inference techniques are gaining increasing attention as general solutions to rigorously calibrate the output of any machine learning algorithm for novelty detection. This paper contributes to the field by developing a novel method for mitigating their algorithmic randomness, leading to an even more interpretable and reliable framework for powerful novelty detection under false discovery rate control. The idea is to leverage suitable conformal e-values instead of p-values to quantify the significance of each finding, which allows the evidence gathered from multiple mutually dependent analyses of the same data to be seamlessly aggregated. Further, the proposed method can reduce randomness without much loss of power, partly thanks to an innovative way of weighting conformal e-values based on additional side information carefully extracted from the same data. Simulations with synthetic and real data confirm this solution can be effective at eliminating random noise in the inferences obtained with state-of-the-art alternative techniques, sometimes also leading to higher power.Comment: 19 pages, 11 figure

Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community