Model-driven description and validation of composite learning content

Authoring of learning content for courseware systems is a complex activity requiring the combination of a range of design and validation techniques. We introduce the CAVIAr courseware models allowing for learning content description and validation. Model-based representation and analysis of different concerns such as the subject domain, learning context, resources and instructional design used are key contributors to this integrated solution. Personalised learning is particularly difficult to design as dynamic configurations cannot easily be predicted and tested. A tool-supported technique based on CAVIAr can alleviate this complexity through the validation of a set of pedagogical and non-pedagogical requirements. Courseware validation checks intra- and inter-content relationships and the compliance with requirements and educational theories

Transforming a competency model to assessment items

The problem of comparing and matching different learners’ knowledge arises when assessment systems use a one-dimensional numerical value to represent “knowledge level”. Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multi-dimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner’s knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses the automatic generation of an assessment from the COMBA competency model as a “guideon- the–side”

Model-driven transformation and validation of adaptive educational hypermedia using CAVIAr

Authoring of Adaptive Educational Hypermedia is a complex activity requiring the combination of a range of design and validation techniques.We demonstrate how Adaptive Educational Hypermedia can be transformed into CAVIAr courseware validation models allowing for its validation. The model-based representation and analysis of different concerns and model-based mappings and transformations are key contributors to this integrated solution. We illustrate the benefits of Model Driven Engineering methodologies that allow for interoperability between CAVIAr and a well known Adaptive Educational Hypermedia framework. By allowing for the validation of Adaptive Educational Hypermedia, the course creator limits the risk of pedagogical problems in migrating to Adaptive Educational Hypermedia from static courseware

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

A Hybrid Recommender Strategy on an Expanded Content Manager in Formal Learning

The main topic of this paper is to find ways to improve learning in a formal Higher Education Area. In this environment, the teacher publishes or suggests contents that support learners in a given course, as supplement of classroom training. Generally, these materials are pre-stored and not changeable. These contents are typically published in learning management systems (the Moodle platform emerges as one of the main choices) or in sites created and maintained on the web by teachers themselves. These scenarios typically include a specific group of students (class) and a given period of time (semester or school year). Contents reutilization often needs replication and its update requires new edition and new submission by teachers. Normally, these systems do not allow learners to add new materials, or to edit existing ones. The paper presents our motivations, and some related concepts and works. We describe the concepts of sequencing and navigation in adaptive learning systems, followed by a short presentation of some of these systems. We then discuss the effects of social interaction on the learners’ choices. Finally, we refer some more related recommender systems and their applicability in supporting learning. One central idea from our proposal is that we believe that students with the same goals and with similar formal study time can benefit from contents' assessments made by learners that already have completed the same courses and have studied the same contents. We present a model for personalized recommendation of learning activities to learners in a formal learning context that considers two systems. In the extended content management system, learners can add new materials, select materials from teachers and from other learners, evaluate and define the time spent studying them. Based on learner profiles and a hybrid recommendation strategy, combining conditional and collaborative filtering, our second system will predict learning activities scores and offers adaptive and suitable sequencing learning contents to learners. We propose that similarities between learners can be based on their evaluation interests and their recent learning history. The recommender support subsystem aims to assist learners at each step suggesting one suitable ordered list of LOs, by decreasing order of relevance. The proposed model has been implemented in the Moodle Learning Management System (LMS), and we present the system’s architecture and design. We will evaluate it in a real higher education formal course and we intend to present experimental results in the near future

An evaluation of pedagogically informed parameterised questions for self assessment

Self-assessment is a crucial component of learning. Learners can learn by asking themselves questions and attempting to answer them. However, creating effective questions is time-consuming because it may require considerable resources and the skill of critical thinking. Questions need careful construction to accurately represent the intended learning outcome and the subject matter involved. There are very few systems currently available which generate questions automatically, and these are confined to specific domains. This paper presents a system for automatically generating questions from a competency framework, based on a sound pedagogical and technological approach. This makes it possible to guide learners in developing questions for themselves, and to provide authoring templates which speed the creation of new questions for self-assessment. This novel design and implementation involves an ontological database that represents the intended learning outcome to be assessed across a number of dimensions, including level of cognitive ability and subject matter. The system generates a list of all the questions that are possible from a given learning outcome, which may then be used to test for understanding, and so could determine the degree to which learners actually acquire the desired knowledge. The way in which the system has been designed and evaluated is discussed, along with its educational benefits

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

The genome and transcriptome of Trichormus sp NMC-1: insights into adaptation to extreme environments on the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) has the highest biodiversity for an extreme environment worldwide, and provides an ideal natural laboratory to study adaptive evolution. In this study, we generated a draft genome sequence of cyanobacteria Trichormus sp. NMC-1 in the QTP and performed whole transcriptome sequencing under low temperature to investigate the genetic mechanism by which T. sp. NMC-1 adapted to the specific environment. Its genome sequence was 5.9 Mb with a G+C content of 39.2% and encompassed a total of 5362 CDS. A phylogenomic tree indicated that this strain belongs to the Trichormus and Anabaena cluster. Genome comparison between T. sp. NMC-1 and six relatives showed that functionally unknown genes occupied a much higher proportion (28.12%) of the T. sp. NMC-1 genome. In addition, functions of specific, significant positively selected, expanded orthogroups, and differentially expressed genes involved in signal transduction, cell wall/membrane biogenesis, secondary metabolite biosynthesis, and energy production and conversion were analyzed to elucidate specific adaptation traits. Further analyses showed that the CheY-like genes, extracellular polysaccharide and mycosporine-like amino acids might play major roles in adaptation to harsh environments. Our findings indicate that sophisticated genetic mechanisms are involved in cyanobacterial adaptation to the extreme environment of the QTP

An E-Learning Investigation into Learning Style Adaptivity

Abstrac

Transforming a competency model to assessment items

The problem of comparing and matching different learners’ knowledge arises when assessment systems use a one-dimensional numerical value to represent “knowledge level”. Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multi-dimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner’s knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses the automatic generation of an assessment from the COMBA competency model as a “guide-on-the–side”