Something for everyone? The different approaches of academic disciplines to Open Educational Resources and the effect on widening participation

This article explores the relationship between academic disciplines‘ representation in the United Kingdom Open University‘s (OU) OpenLearn open educational resources (OER) repository and in the OU‘s fee-paying curriculum. Becher‘s (1989) typology was used to subdivide the OpenLearn and OU fee-paying curriculum content into four disciplinary categories: Hard Pure (e.g., Science), Hard Applied (e.g., Technology), Soft Pure (e.g., Arts) and Soft Applied (e.g., Education). It was found that while Hard Pure and Hard Applied disciplines enjoy an increased share of the OER curriculum, Soft Applied disciplines are under-represented as OER. Possible reasons for this disparity are proposed and Becher‘s typology is adapted to be more appropriate to 21st-century higher education

Lifelong Spectral Clustering

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks. In this paper, we aim to explore the problem of spectral clustering in a lifelong machine learning framework, i.e., Lifelong Spectral Clustering (L2SC). Its goal is to efficiently learn a model for a new spectral clustering task by selectively transferring previously accumulated experience from knowledge library. Specifically, the knowledge library of L2SC contains two components: 1) orthogonal basis library: capturing latent cluster centers among the clusters in each pair of tasks; 2) feature embedding library: embedding the feature manifold information shared among multiple related tasks. As a new spectral clustering task arrives, L2SC firstly transfers knowledge from both basis library and feature library to obtain encoding matrix, and further redefines the library base over time to maximize performance across all the clustering tasks. Meanwhile, a general online update formulation is derived to alternatively update the basis library and feature library. Finally, the empirical experiments on several real-world benchmark datasets demonstrate that our L2SC model can effectively improve the clustering performance when comparing with other state-of-the-art spectral clustering algorithms.Comment: 9 pages,7 figure

Análisis de clúster de perspectivas de participantes en MOOC

Els cursos en línia massius i oberts (Massive Open Online Courses, MOOC) proporcionen oportunitats il·limitades per a la participació de milers d’estudiants en cursos d’ensenyament superior en línia. Els MOOC tenen característiques úniques que els converteixen en un mètode efectiu de l’aprenentatge electrònic, en concret l’aprenentatge millorat per tecnologia (Technology-Enhanced Learning, TEL). Nombroses institucions ofereixen una creixent varietat de MOOC. No obstant això, existeixen múltiples reptes que han ser considerats en desenvolupar MOOC, per exemple, la taxa d’abandonament de participants en els cursos és del 95%. Una de les possibles raons és la complexitat i la diversitat dels participants en els MOOC. Aquesta diversitat no està només relacionada amb el perfil demogràfic i cultural, sinó també amb els diversos motius i perspectives que els usuaris tenen en inscriure’s en MOOC. La intenció d’aquest article és agrupar en clústers els objectius dels participants en MOOC i analitzar-los per aconseguir una millor comprensió dels seus comportaments. El principal resultat és el descobriment de vuit clústers: aprenentatge mesclat (blended learning), flexibilitat (flexibility), contingut d’alta qualitat (high quality content), disseny instruccional i metodologies d’aprenentatge (instructional design & learning methodologies), aprenentatge al llarg de la vida (lifelong learning), aprenentatge en xarxa (network learning), obertura (openness) i aprenentatge centrat en l’estudiant (student-centered learning). Aquest esquema d’agrupament en clústers crea una visió significativa per a la comunitat de participants en MOOC.Massive Open Online Courses (MOOCs) are providing opportunities for thousands of learners to participate in free higher education courses online. MOOCs have unique features that make them an effective Technology-Enhanced Learning (TEL) approach. Institutions are offering a growing variety of MOOCs. Nevertheless, there are several crucial challenges that should be considered in the development of MOOCs, e.g., the drop-out rate of over 95% of course participants. One of the potential reasons for that is the complexity and diversity of MOOC participants. This diversity is not only related to the cultural and demographic profile, but also considers the diverse motives and perspectives when enrolled in MOOCs. This paper aims to cluster and analyze the different objectives of MOOC stakeholders to build a deeper and better understanding of their behaviors. Our main finding was a set of eight clusters, i.e., blended learning, flexibility, high quality content, instructional design and learning methodologies, lifelong learning, network learning, openness, and student-centered learning. This cluster schema creates a meaningful picture for the MOOC community.Los cursos en línea masivos y abiertos (Massive Open Online Courses, MOOC) proporcionan oportunidades ilimitadas para la participación de miles de estudiantes en cursos de enseñanza superior en línea. Los MOOC tienen características únicas que los convierten en un método efectivo del aprendizaje electrónico, en concreto el aprendizaje mejorado por tecnología (Technology-Enhanced Learning, TEL). Numerosas instituciones ofrecen una creciente variedad de MOOC. Sin embargo, existen múltiples retos que deben ser considerados al desarrollar MOOC, por ejemplo, la tasa de abandono de participantes en los cursos es del 95%. Una de las posible razones para ello es la complejidad y la diversidad de los participantes en los MOOC. Esta diversidad no está solamente relacionada con el perfil demográfico y cultural, sino también con los diversos motivos y perspectivas que los usuarios tienen al inscribirse en MOOC. La intención de este artículo es agrupar en clústeres los objetivos de los participantes en MOOC y analizarlos para lograr una mayor comprensión de sus comportamientos. El principal resultado es el descubrimiento de ocho clústeres: aprendizaje mezclado (blended learning), flexibilidad (flexibility), contenido de alta calidad (high quality content), diseño instruccional y metodologías de aprendizaje (instructional design & learning methodologies), aprendizaje a lo largo de la vida (lifelong learning), aprendizaje en red (network learning), apertura (openness) y aprendizaje centrado en el estudiante (student-centered learning). Este esquema de agrupamiento en clústeres crea una visión significativa para la comunidad de participantes en MOOC

Developing the scales on evaluation beliefs of student teachers

The purpose of the study reported in this paper was to investigate the validity and the reliability of a newly developed questionnaire named ‘Teacher Evaluation Beliefs’ (TEB). The framework for developing items was provided by the two models. The first model focuses on Student-Centered and Teacher-Centered beliefs about evaluation while the other centers on five dimensions (what/ who/ when/ why/ how). The validity and reliability of the new instrument was investigated using both exploratory and confirmatory factor analysis study (n=446). Overall results indicate that the two-factor structure is more reasonable than the five-factor one. Further research needs additional items about the latent dimensions “what” ”who” ”when” ”why” “how” for each existing factor based on Student-centered and Teacher-centered approaches