Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

Neuroverkkopohjainen faktoidikysymyksiin vastaaminen ja kysymysten generointi suomen kielellä

Automaattinen kysymyksiin vastaaminen ja kysymysten generointi ovat kaksi tiiviisti toisiinsa liittyvää luonnollisen kielen käsittelyn tehtävää. Molempia tehtäviä on tutkittu useiden vuosikymmenten ajan ja niillä on useita käyttökohteita. Järjestelmät, jotka osaavat vastata luonnollisella kielellä muodostettuihin kysymyksiin toimivat apuna ihmisten informaatiotarpeissa, kun taas automaattista kysymysten generointia voidaan hyödyntää muun muassa luetunymmärtämistehtävien automaattisessa luomisessa sekä virtuaaliassistenttien interaktiivisuuden parantamisessa. Sekä kysymyksiin vastaamisessa että niiden generoinnissa parhaat tulokset saadaan tällä hetkellä hyödyntämällä esikoulutettuja, transformer-arkkitehtuuriin pohjautuvia neuraalisia kielimalleja. Tällaiset mallit tyypillisesti ensin esikoulutetaan raa’alla kielidatalla ja sitten hienosäädetään erilaisiin tehtäviin käyttäen tehtäväkohtaisia annotoituja aineistoja. Malleja, jotka osaavat vastata suomenkielisiin kysymyksiin tai generoida niitä, ei ole tähän mennessä raportoitu juurikaan olevan olemassa. Jotta niitä voitaisiin luoda moderneja transformer-arkkitehtuuriin perustuvia menetelmiä käyttäen, tarvitaan sekä esikoulutettu kielimalli että tarpeeksi suuri määrä suomenkielistä dataa, joka soveltuu esikoulutettujen mallien hienosäätämiseen juuri kysymyksiin vastaamiseen tai generointiin. Vaikka sekä puhtaasti suomen kielellä esikoulutettuja yksikielisiä malleja että osittain suomen kielellä esikoulutettuja monikielisiä malleja onkin jo jonkin verran avoimesti saatavilla, ongelmaksi muodostuu hienosäätöön tarvittavan datan puuttuminen. Tässä tutkielmassa luodaan ensimmäiset suomenkieliset transformer-arkkitehtuuriin pohjautuvat kysymyksiin vastaamiseen ja kysymysten generointiin hienosäädetyt neuroverkkomallit. Esittelen menetelmän, jolla pyritään luomaan aineisto, joka soveltuu esikoulutettujen mallien hienosäätämiseen molempiin edellä mainittuihin tehtäviin. Aineiston luonti perustuu olemassa olevan englanninkielisen SQuAD-aineiston koneelliseen kääntämiseen sekä käännöksen jälkeisten automaattisten normalisointimenetelmien käyttöön. Hienosäädän luodun aineiston avulla useita esikoulutettuja malleja suomenkieliseen kysymyksiin vastaamiseen ja kysymysten generointiin, sekä vertailen niiden suorituskykyä. Käytän sekä puhtaasti suomen kielellä esikoulutettuja BERT- ja GPT-2-malleja että yhtä monikielisellä aineistolla esikoulutettua BERT-mallia. Tulokset osoittavat, että transformer-arkkitehtuuri soveltuu hyvin myös suomenkieliseen kysymyksiin vastaamiseen ja kysymysten generointiin. Synteettisesti luotu aineisto on tulosten perusteella käyttökelpoinen resurssi esikoulutettujen mallien hienosäätämiseen. Parhaat tulokset molemmissa tehtävissä tuottavat hienosäädetyt BERT-mallit, jotka on esikoulutettu ainoastaan suomenkielisellä kieliaineistolla. Monikielisen BERT:n tulokset ovat lähes yhtä hyviä molemmissa tehtävissä, kun taas GPT-2-mallien tulokset ovat reilusti huonompia.Automatic question answering and question generation are two closely related natural language processing tasks. They both have been studied for decades, and both have a wide range of uses. While systems that can answer questions formed in natural language can help with all kinds of information needs, automatic question generation can be used, for example, to automatically create reading comprehension tasks and improve the interactivity of virtual assistants. These days, the best results in both question answering and question generation are obtained by utilizing pre-trained neural language models based on the transformer architecture. Such models are typically first pre-trained with raw language data and then fine-tuned for various tasks using task-specific annotated datasets. So far, no models that can answer or generate questions purely in Finnish have been reported. In order to create them using modern transformer-based methods, both a pre-trained language model and a sufficiently big dataset suitable for question answering or question generation fine-tuning are required. Although some suitable models that have been pre-trained with Finnish or multilingual data are already available, a big bottleneck is the lack of annotated data needed for fine-tuning the models. In this thesis, I create the first transformer-based neural network models for Finnish question answering and question generation. I present a method for creating a dataset for fine-tuning pre-trained models for the two tasks. The dataset creation is based on automatic translation of an existing dataset (SQuAD) and automatic normalization of the translated data. Using the created dataset, I fine-tune several pre-trained models to answer and generate questions in Finnish and evaluate their performance. I use monolingual BERT and GPT-2 models as well as a multilingual BERT model. The results show that the transformer architecture is well suited also for Finnish question answering and question generation. They also indicate that the synthetically generated dataset can be a useful fine-tuning resource for these tasks. The best results in both tasks are obtained by fine-tuned BERT models which have been pre-trained with only Finnish data. The fine-tuned multilingual BERT models come in close, whereas fine-tuned GPT-2 models are generally found to underperform. The data developed for this thesis will be released to the research community to support future research on question answering and generation, and the models will be released as benchmarks

A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Canonical explorations of 'Tel' environments for computer programming

This paper applies a novel technique of canonical gradient analysis, pioneered in ecological sciences, with the aim of exploring student performance and behaviours (such as communication and collaboration) while undertaking formative and summative tasks in technology enhanced learning (TEL) environments for computer programming. The research emphasis is, therefore, on revealing complex patterns, trends, tacit communications and technology interactions associated with a particular type of learning environment, rather than the testing of discrete hypotheses. The study is based on observations of first year programming modules in BSc Computing and closely related joint-honours with software engineering, web and game development courses. This research extends earlier work, and evaluates the suitability of canonical approaches for exploring complex dimensional gradients represented by multivariate and technology-enhanced learning environments. The advancements represented here are: (1) an extended context, beyond the use of the ‘Ceebot’ learning platform, to include learning-achievement following advanced instruction using an industrystandard integrated development environment, or IDE, for engineering software; and (2) longitudinal comparison of consistency of findings across cohort years. Direct findings (from analyses based on code tests, module assessment and questionnaire surveys) reveal overall engagement with and high acceptance of collaborative working and of the TEL environments used, but an inconsistent relationship between deeply learned programming skills and module performance. The paper also discusses research findings in the contexts of established and emerging teaching practices for computer programming, as well as government policies and commercial requirements for improved capacity in computer-science related industries

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

The student experience of a blended learning accounting course: a case study in Hong Kong

The research is an inquiry into students’ learning experiences within a blended learning Accounting course in a sub-degree programme at a university in Hong Kong. In this course, the students were required to attend face-to-face classes and to participate in learning activities in the online platform. A case study research approach was adopted that involved 2 classes of 2 teachers and 80 students. Qualitative data were generated through classroom observations, online participation observations, student learning logs and reflections, student focus group interviews, student individual interviews, individual teacher interviews and an individual interview with the course leader. Thematic data analysis was used and a Community of Inquiry (CoI) model was used as a theoretical framework. The analysis showed that the students engaged in learning by integrating traditional and online learning activities and many of these were located within the social, cognitive and teaching presences within the CoI model. However, the students were found to be involved actively in non-prescribed activities that included the use of social network applications. The active learning exploration driven by students’ intrinsic motivation and the consequent collaborative learning among students using social media tools were not reflected in the CoI model. Hence, a new element of autonomy is proposed as an addition to the framework, to reveal the link of autonomous learning to the learning community. By extending the CoI framework, the contribution of this research is to provide a holistic model for the successful design and implementation of blended learning in higher education institutions

The student experience of a blended learning accounting course: a case study in Hong Kong

The research is an inquiry into students’ learning experiences within a blended learning Accounting course in a sub-degree programme at a university in Hong Kong. In this course, the students were required to attend face-to-face classes and to participate in learning activities in the online platform. A case study research approach was adopted that involved 2 classes of 2 teachers and 80 students. Qualitative data were generated through classroom observations, online participation observations, student learning logs and reflections, student focus group interviews, student individual interviews, individual teacher interviews and an individual interview with the course leader. Thematic data analysis was used and a Community of Inquiry (CoI) model was used as a theoretical framework. The analysis showed that the students engaged in learning by integrating traditional and online learning activities and many of these were located within the social, cognitive and teaching presences within the CoI model. However, the students were found to be involved actively in non-prescribed activities that included the use of social network applications. The active learning exploration driven by students’ intrinsic motivation and the consequent collaborative learning among students using social media tools were not reflected in the CoI model. Hence, a new element of autonomy is proposed as an addition to the framework, to reveal the link of autonomous learning to the learning community. By extending the CoI framework, the contribution of this research is to provide a holistic model for the successful design and implementation of blended learning in higher education institutions