KFTKGA: Knowledge Flow Tracer and Growth Analyzer for Community Learning

The advances in building learning technology now have to emphasize on the aspect of the individual learning besides the popular focus on the technology per se. Unlike the common research where a great deal has been on finding ways to build, manage, classify, categorize and search knowledge on the server, there is an interest in our work to look at the knowledge development at the individual’s learning. We build the technology that resides behind the knowledge sharing platform where learning and sharing activities of an individual take place. The system that we built, KFTGA (Knowledge Flow Tracer and Growth Analyzer), demonstrates the capability of identifying the topics and subjects that an individual is engaged with during the knowledge sharing session and measuring the knowledge growth of the individual learning on a specific subject on a given time space.

Effects of real -time captioning and sign language interpreting on the learning of college students who are deaf or hard of hearing

College personnel are required to provide accommodations for students who are deaf and hard of hearing (D/HoH), but few empirical studies have been conducted on D/HoH students as they learn under the various accommodation conditions (sign language interpreting, SLI, real-time captioning, RTC, and both). Guided by the experiences of students who are D/HoH at Miami-Dade College (MDC) who requested RTC in addition to SLI as accommodations, the researcher adopted Merten’s transformative-emancipatory theoretical framework that values perceptions and voice of students who are D/HoH. A mixed methods design addressed two research questions: Did student learning differ for each accommodation? What did students experience while learning through accommodations? Participants included 30 students who were D/HoH (60% women). They represented MDC’s majority minority population: 10% White (non-Hispanic), 20% Black (non-Hispanic, including Haitian/Caribbean), 67% Hispanic, and 3% other. Hearing loss, ranged from severe-profound (70%) to mild-moderate (30%). All were able to communicate with American Sign Language: Learning was measured while students who were D/HoH viewed three lectures under three accommodation conditions (SLI, RTC, SLI+RTC). The learning measure was defined as the difference in pre- and post-test scores on tests of the content presented in the lectures. Using repeated measure ANOVA and ANCOVA, confounding variables of fluency in American Sign Language and literacy skills were treated as covariates. Perceptions were obtained through interviews and verbal protocol analysis that were signed, videotaped, transcribed, coded, and examined for common themes and metacognitive strategies. No statistically significant differences were found among the three accommodations on the learning measure. Students who were D/HoH expressed thoughts about five different aspects of their learning while they viewed lectures: (a) comprehending the information, (b) feeling a part of the classroom environment, (c) past experiences with an accommodation, (d) individual preferences for an accommodation, (e) suggestions for improving an accommodation. They exhibited three metacognitive strategies: (a) constructing knowledge, (b) monitoring comprehension, and (c) evaluating information. No patterns were found in the types of metacognitive strategies used for any particular accommodation. The researcher offers recommendations for flexible applications of the standard accommodations used with students who are D/HoH.

Taiwan Discovered: In Place and Time

Catalog of an exhibition held at the Frost Art Museum, Florida International University. Curated by Carol Damian and Catalina Jaramillo.

The first time delivery of the first link learning series using Skype and YouTube

Introduction: This case study documented the experiences of informal and service providers who participated in the first time delivery of the First Link Learning Series from May–August 2013 in Newfoundland and Labrador. The aim of this study was to understand how informal caregivers of people with dementia experience this Internet mediated health resource, and how Skype and YouTube can be used as tools for the Alzheimer Society of Newfoundland and Labrador to effectively deliver the First Link Learning Series. Methods: Sources of data included key informant interviews (n=3), pre- study and post-study interviews with informal dementia caregivers (n=2), institutional documentation, field notes, and YouTube analytics. Framework Analysis was used to make meaning of the qualitative data, and descriptive statistics were used to report on quantitative outcomes. Findings: Between 3% and 17% of registered First Link clients attended the learning series sessions, however only two caregivers participated using Skype or YouTube. Framework Analysis revealed three shared themes: access, connection and privacy. Discussion: The themes helped to begin building theory about barriers and facilitators to Internet mediated health resources for informal dementia caregivers. Experiences of service providers using the Internet to support clients served to begin building a case for the appropriateness of these media. A modified version of Dansky et al.’s (2006) theoretical framework for evaluating E-Health research that situates the person/user in the model, helped guide discussion and propose future directions for the study of Internet based health resources for informal dementia caregivers.

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

Learning to 'understand backwards' in time: children's temporal cognition and the primary history curriculum

This study examines children’s temporal ways of knowing and it highlights the centrality of temporal cognition in the development of children’s historical understanding. It explores how young children conceptualise time and it examines the provision for temporal cognition at the levels of the intended, enacted and received history curriculum in the Irish primary school context. Positioning temporality as a prerequisite second-order concept, the study recognises the essential role of both first-order and additional second-order concepts in historical understanding. While the former can be defined as the basic, substantive content to be taught, the latter refers to a number of additional key concepts that are deemed fundamental to children's capacity to make meaningful sense of history. The study argues for due recognition to be given to temporality, in the belief that both sets of knowledge, the content and skills, are required to develop historical thinking (Lévesque, 2011). The study addresses a number of key research questions, using a mixed methods research design, comprising an analysis of history textbooks, a survey among final year student teachers about their teaching of history, and school-based interviews with primary school children: What opportunities are available for children to develop temporal ways of knowing? How do student teachers experience being apprenticed into the available culture for teaching history and understanding temporality at primary level? What insights do the cognitive-developmental and sociocultural perspectives on learning provide for understanding the dynamics of children’s temporal ways of knowing? The study argues that the skill of developing a deeper understanding of time is a key prerequisite in connecting with, and constructing, understandings and frameworks of the past. The study advances a view of temporality as complex, multi-faceted and developmental. The findings have a potential contribution to make in influencing policy and pedagogy in establishing an elaborated and well-defined curriculum framework for developing temporal cognition at both national and international levels.

Westminster Serious Games Platform (wmin-SGP) a tool for real-time authoring of roleplay simulations for learning

The use of serious games in education and their pedagogical benefit is being widely recognized. However, effective integration of serious games in education depends on addressing two big challenges: the successful incorporation of motivation and engagement that can lead to learning; and the highly specialised skills associated with customised development to meet the required pedagogical objectives. This paper presents the Westminster Serious Games Platform (wmin-SGP) an authoring tool that allows educators/domain experts without games design and development technical skills to create bespoke roleplay simulations in three dimensional scenes featuring fully embodied virtual humans capable of verbal and non-verbal interaction with users fit for specific educational objectives. The paper presents the wmin-SGP system architecture and it evaluates its effectiveness in fulfilling its purpose via the implementation of two roleplay simulations, one for Politics and one for Law. In addition, it presents the results of two types of evaluation that address how successfully the wmin-SGP combines usability principles and game core drives based on the Octalysis gamification framework that lead to motivating games experiences. The evaluation results shows that the wmin-SGP: provides an intuitive environment and tools that support users without advanced technical skills to create in real-time bespoke roleplay simulations in advanced graphical interfaces; satisfies most of the usability principles; and provides balanced simulations based on the Octalysis framework core drives. The paper concludes with a discussion of future extension of this real time authoring tool and directions for further development of the Octalysis framework to address learning.

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

The Hidden benefits of “poster presentation assessment”: Skill acquirement and learning; evaluated from the nursing students perspective

Introduction
This paper reports to an exercise in evaluating poster group work and poster presentation and the extra learning and skill acquisition that this can provide to nursing students, through a creative and stimulating assessment method. Much had been written about the benefits of using posters as an assessment method, yet there appears to be a lack of research that captures the student experience.
Aim
This evaluative study sought to evaluate the student experience by using a triangulation approach to evaluation:
Methodology
All students from the February 2015 nursing intake, were eligible to take part (80 students) of which 71 participated (n=71). The poster group presentations took place at the end of their first phase of year one teaching and the evaluation took place at the end of their first year as undergraduate. Evaluation involved;
1. Quantitative data by questionnaires
2. Qualitative data from focus group discussions
Results
A number of key themes emerged from analysis of the data which captured the “added value” of learning from the process of poster assessment including:
 Professionalism: developing time keeping skills, presenting skills.
 Academic skills: developing literature search, critic and reporting
 Team building and collaboration
Overall 88% agreed that the process furnished them with additional skills and benefits above the actual production of the poster, with 97% agreeing that these additional skills are important skills for a nurse.
Conclusion
These results would suggest that the process of poster development and presentation furnish student nurses with many additional skills that they may not acquire through other types of assessment and are therefore beneficial. The structure of the assessment encourages a self-directed approach so students take control of the goals and purposes of learning. The sequential organization of the assessment guides students in the transition from dependent to self-directed learners.

Towards an understanding of Pakistani undergraduates’ current attitudes towards learning and speaking English

The English language has an important place in Pakistan and in its education system, not least because of the global status of English and its role in employment. Realising the need to enhance language learning outcomes, especially at the tertiary level, the Higher Education Commission (HEC) of Pakistan has put in place some important measures to improve the quality of English language teaching practice through its English Language Teaching Reforms (ELTR) project. However, there is a complex linguistic, educational and ethnic diversity in Pakistan and that diversity, alongside the historical and current role of English in the country, makes any language teaching reform particularly challenging. I argue, in this thesis, that reform to date has largely ignored the issues of learner readiness to learn and learner perceptions of the use of English. I argue that studying learner attitudes is important if we are to understand how learners perceive the practice of learning and the use of English in their lives. This study focuses on the attitudes of undergraduate learners of English as a foreign language at two universities in the provinces of Sindh and Balochistan in Pakistan. These provinces have experienced long struggles and movements related to linguistic and ethnic rights and both educate students from all of the districts of their respective provinces. Drawing on debates around linguistic imperialism, economic necessity, and linguistic and educational diversity, I focus on learners’ perceptions about learning and speaking English, asking what their attitudes are towards learning and speaking English with particular reference to socio-psychological factors at a given time and context, including perceived threats to their culture, religion, and mother tongue. I ask how they make choices about learning and speaking English in different domains of language use and question their motivation to learn and speak English. Additionally, I explore issues of anxiety with reference to their use of English. Following a predominantly qualitative mixed methods research approach, the study employs two research tools: an adapted Likert Scale questionnaire completed by 300 students and semi-structured interviews with 20 participants from the two universities. The data were analysed through descriptive statistics and qualitative content analysis, with each set of data synthesised for interpretation. The findings suggest that, compared with the past, the majority of participants hold positive attitudes towards learning and speaking English regardless of their ethnic or linguistic backgrounds. Most of these undergraduate students do not perceive the use of English as a threat to their culture, mother tongue or religious values but, instead, they have a pragmatic and, at the same time, aspirational attitude to the learning and use of English. I present these results and conclude this thesis with reference to ways in which this small-scale study contributes to a better understanding of learner attitudes and perceptions. Acknowledging the limitations of this study, I suggest ways in which the study, enhanced and extended by further research, might have implications for practice, theory and policy in English language teaching and learning in Pakistan.

Real-time audio interaction in serious games for music learning

In this LBD, we present several Apps for playing while learning music or for learning music while playing. The core of all the games is based on the good performance of the real-time audio interaction algorithms developed by the ATIC group at Universidad de Ma ́laga (SPAIN).

Part-time and fractional staff in the learning and skills sector

This Universities and College Union Launch Event presentation reported on the findings of Learning and Skills Research Network (LSRN) London and South East (LSE) Regional Research Project. The presentation reflected on research carried out during 2002-06 on the development and deployment of part-time staff in the Learning and Skills Sector. Although the lifelong learning sector is the largest UK education sector, little attention has as yet been paid to the role of LSC sector part-time staff. Worrying trends of an increasing casualisation of staffing have been reported. The role of part-timers as highly committed (philanthropic) but generally underpaid and exploited staff (ragged-trousered) emerged from the data collected by this investigation, which examined the role of part-timers in several colleges and adult education institutions in London and the South East. The metaphor of the 'ragged-trousered philanthropist' was consciously selected to investigate the interactivity between philantrophy, employment practices for PT staff, and education as social action, in addressing the need for good practice to achieve quality outcomes in learning and teaching. The results are to some extent transferable to other education and training sectors employing part-time staff, e.g. higher education institutions and work-based training organisations.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Variational and learning models for image and time series inverse problems

Inverse problems are at the core of many challenging applications. Variational and learning models provide estimated solutions of inverse problems as the outcome of specific reconstruction maps. In the variational approach, the result of the reconstruction map is the solution of a regularized minimization problem encoding information on the acquisition process and prior knowledge on the solution. In the learning approach, the reconstruction map is a parametric function whose parameters are identified by solving a minimization problem depending on a large set of data. In this thesis, we go beyond this apparent dichotomy between variational and learning models and we show they can be harmoniously merged in unified hybrid frameworks preserving their main advantages. We develop several highly efficient methods based on both these model-driven and data-driven strategies, for which we provide a detailed convergence analysis. The arising algorithms are applied to solve inverse problems involving images and time series. For each task, we show the proposed schemes improve the performances of many other existing methods in terms of both computational burden and quality of the solution. In the first part, we focus on gradient-based regularized variational models which are shown to be effective for segmentation purposes and thermal and medical image enhancement. We consider gradient sparsity-promoting regularized models for which we develop different strategies to estimate the regularization strength. Furthermore, we introduce a novel gradient-based Plug-and-Play convergent scheme considering a deep learning based denoiser trained on the gradient domain. In the second part, we address the tasks of natural image deblurring, image and video super resolution microscopy and positioning time series prediction, through deep learning based methods. We boost the performances of supervised, such as trained convolutional and recurrent networks, and unsupervised deep learning strategies, such as Deep Image Prior, by penalizing the losses with handcrafted regularization terms.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.