Applying Recommender Systems and Adaptive Hypermedia for e-Learning Personalizatio

Learners learn differently because they are different -- and they grow more distinctive as they mature. Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper we present our design and implementation of an adaptive and intelligent web-based programming tutoring system -- Protus, which applies recommendation and adaptive hypermedia techniques. This system aims at automatically guiding the learner's activities and recommend relevant links and actions to him/her during the learning process. Experiments on real data sets show the suitability of using both recommendation and hypermedia techniques in order to suggest online learning activities to learners based on their preferences, knowledge and the opinions of the users with similar characteristics

Wide-Scale Automatic Analysis of 20 Years of ITS Research

The analysis of literature within a research domain can provide significant value during preliminary research. While literature reviews may provide an in-depth understanding of current studies within an area, they are limited by the number of studies which they take into account. Importantly, whilst publications in hot areas abound, it is not feasible for an individual or team to analyse a large volume of publications within a reasonable amount of time. Additionally, major publications which have gained a large number of citations are more likely to be included in a review, with recent or fringe publications receiving less inclusion. We provide thus an automatic methodology for the large-scale analysis of literature within the Intelligent Tutoring Systems (ITS) domain, with the aim of identifying trends and areas of research from a corpus of publications which is significantly larger than is typically presented in conventional literature reviews. We illustrate this by a novel analysis of 20 years of ITS research. The resulting analysis indicates a significant shift of the status quo of research in recent years with the advent of novel neural network architectures and the introduction of MOOCs

Template-driven teacher modelling approach : a thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Science at Massey University, Palmerston North

This thesis describes the Template-driven Teacher Modeling Approach, the initial implementation of the template server and the formative evaluation on the prototype. The initiative of Template-driven teacher modeling is to integrate the template server and intelligent teacher models in Web-based education systems for course authoring. There are a number of key components in the proposed system: user interface, template server and content repository. The Template-Driven Teacher Modeling (TDTM) architecture supports the course authoring by providing higher degree of control over the generation of presentation. The collection of accumulated templates in the template repository for a teacher or a group of teachers are selected as the inputs for the inference mechanism in teacher's model to calculate the best representation of the teaching strategy, and then predict teacher intention when he or she interacts with the system. Moreover, the presentation templates are kept to support the re-use of the on-line content at the level of individual screens with the help of Template Server

Designing a training tool for imaging mental models

The training process can be conceptualized as the student acquiring an evolutionary sequence of classification-problem solving mental models. For example a physician learns (1) classification systems for patient symptoms, diagnostic procedures, diseases, and therapeutic interventions and (2) interrelationships among these classifications (e.g., how to use diagnostic procedures to collect data about a patient's symptoms in order to identify the disease so that therapeutic measures can be taken. This project developed functional specifications for a computer-based tool, Mental Link, that allows the evaluative imaging of such mental models. The fundamental design approach underlying this representational medium is traversal of virtual cognition space. Typically intangible cognitive entities and links among them are visible as a three-dimensional web that represents a knowledge structure. The tool has a high degree of flexibility and customizability to allow extension to other types of uses, such a front-end to an intelligent tutoring system, knowledge base, hypermedia system, or semantic network

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

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

Supporting teachers in collaborative student modeling: a framework and an implementation

Collaborative student modeling in adaptive learning environments allows the learners to inspect and modify their own student models. It is often considered as a collaboration between students and the system to promote learners’ reflection and to collaboratively assess the course. When adaptive learning environments are used in the classroom, teachers act as a guide through the learning process. Thus, they need to monitor students’ interactions in order to understand and evaluate their activities. Although, the knowledge gained through this monitorization can be extremely useful to student modeling, collaboration between teachers and the system to achieve this goal has not been considered in the literature. In this paper we present a framework to support teachers in this task. In order to prove the usefulness of this framework we have implemented and evaluated it in an adaptive web-based educational system called PDinamet.Postprint (author's final draft

A data-assisted approach to supporting instructional interventions in technology enhanced learning environments

The design of intelligent learning environments requires significant up-front resources and expertise. These environments generally maintain complex and comprehensive knowledge bases describing pedagogical approaches, learner traits, and content models. This has limited the influence of these technologies in higher education, which instead largely uses learning content management systems in order to deliver non-classroom instruction to learners. This dissertation puts forth a data-assisted approach to embedding intelligence within learning environments. In this approach, instructional experts are provided with summaries of the activities of learners who interact with technology enhanced learning tools. These experts, which may include instructors, instructional designers, educational technologists, and others, use this data to gain insight into the activities of their learners. These insights lead experts to form instructional interventions which can be used to enhance the learning experience. The novel aspect of this approach is that the actions of the intelligent learning environment are now not just those of the learners and software constructs, but also those of the educational experts who may be supporting the learning process. The kinds of insights and interventions that come from application of the data-assisted approach vary with the domain being taught, the epistemology and pedagogical techniques being employed, and the particulars of the cohort being instructed. In this dissertation, three investigations using the data-assisted approach are described. The first of these demonstrates the effects of making available to instructors novel sociogram-based visualizations of online asynchronous discourse. By making instructors aware of the discussion habits of both themselves and learners, the instructors are better able to measure the effect of their teaching practice. This enables them to change their activities in response to the social networks that form between their learners, allowing them to react to deficiencies in the learning environment. Through these visualizations it is demonstrated that instructors can effectively change their pedagogy based on seeing data of their students’ interactions. The second investigation described in this dissertation is the application of unsupervised machine learning to the viewing habits of learners using lecture capture facilities. By clustering learners into groups based on behaviour and correlating groups with academic outcome, a model of positive learning activity can be described. This is particularly useful for instructional designers who are evaluating the role of learning technologies in programs as it contextualizes how technologies enable success in learners. Through this investigation it is demonstrated that the viewership data of learners can be used to assist designers in building higher level models of learning that can be used for evaluating the use of specific tools in blended learning situations. Finally, the results of applying supervised machine learning to the indexing of lecture video is described. Usage data collected from software is increasingly being used by software engineers to make technologies that are more customizable and adaptable. In this dissertation, it is demonstrated that supervised machine learning can provide human-like indexing of lecture videos that is more accurate than current techniques. Further, these indices can be customized for groups of learners, increasing the level of personalization in the learning environment. This investigation demonstrates that the data-assisted approach can also be used by application developers who are building software features for personalization into intelligent learning environments. Through this work, it is shown that a data-assisted approach to supporting instructional interventions in technology enhanced learning environments is both possible and can positively impact the teaching and learning process. By making available to instructional experts the online activities of learners, experts can better understand and react to patterns of use that develop, making for a more effective and personalized learning environment. This approach differs from traditional methods of building intelligent learning environments, which apply learning theories a priori to instructional design, and do not leverage the in situ data collected about learners