Defining and Classifying Learning Outcomes: A case study

Bologne came to globalize education in higher education, creating a unified architecture that potentiates higher education and enhances the continued interconnection of the spaces of education policy in higher education in the world, in particular in Europe. The aim of this work consists in the presentation of an identification model and skills’ classification and learning outcomes, based on the official documents of the course units (syllabus and assessment components) of a course of Higher Education. We are aware that the adoption of this model by different institutions, will contribute to interoperability learning outcomes, thus enhancing the mobility of teachers and students in the EHEA (European Higher Education Area) and third countries

Personalization and User Modeling in Adaptive E-Learning Systems for Schools

The manuscript presents a model for the personalization of e-Learning systems in secondary schools. Approaches are discussed about the implementation of this model by the application of the SCORM-standard, ITL (ITL-Interval and temporal logic), policies, etc. Comments on the possibilities for increasing the relevance of e-Learning systems in the real classroom environment schools are also included

Modelling The Learner Model Based Ontology In Adaptive Learning Environment

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research.   Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits.Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Mode

Student model initialization using domain knowledge ontology representative subset

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is described. The approach defines which concepts from domain knowledge should be included in the initial test so the system can make conclusions about what students truly know about domain knowledge. This representative subset of domain knowledge is defined using non-semantic mathematical approach based on graph theory. The initial test, created over a domain knowledge representative subset, guarantees encompassing all concepts that are relevant to domain knowledge. A two-level case study is conducted on what would be the representative subset of one selected domain knowledge. It compares semantically selected domain knowledge representative subsets (semantical analysis was done by domain area experts) to a non-semantical, mathematically selected domain knowledge representative subset. The results of the case study show that problems of inequality of semantically selected domain knowledge representative subsets are easily overcome using the presented approachPeer Reviewe

A case-based system for lesson plan construction

Planning for teaching imposes a significant burden on teachers, as teachers need to prepare different lesson plans for different classes according to various constraints. Statistical evidence shows that lesson planning in the Malaysian context is done in isolation and lesson plan sharing is limited. The purpose of this thesis is to investigate whether a case-based system can reduce the time teachers spend on constructing lesson plans. A case-based system was designed SmartLP. In this system, a case consists of a problem description and solution pair and an attributevalue representation for the case is used. SmartLP is a synthesis type of CBR system which attempts to create a new solution by combining parts of previous solutions in the adaptation. Five activities in the CBR cycle retrieve, reuse, revise, review and retain are created via three types of design: application, architectural and user interface. The inputs are the requirements and constraints of the curriculum and the student facilities available, and the output is the solution, i.e. appropriate elements of a lesson plan. The retrieval module consists of five types of search advanced search, hierarchical, Boolean, basic and browsing. Solving a problem in this system involves obtaining a problem description, measuring the similarity of the current problem to previous problems stored in a database, retrieving one or more similar cases and attempting to reuse the solution of the retrieved cases, possibly after adaptation. Case adaptation for multiple lesson plans helps teachers to customise the retrieved plan to suit their constraints. This is followed by case revision, which allows users to access and revise their constructed lesson plans in the system. Validation mechanisms, through case verification, ensure that the retained cases are of quality. A formative study was conducted to investigate the effects of SmartLP on performance. The study revealed that all the lesson plans constructed with SmartLP assistance took significantly less time than the control lesson plans constructed without SmartLP assistance, although they might have access to computers and other tools. No significant difference in writing quality, measured by a scoring system, was noticed for the control group, who constructed lesson plans on the same tasks without receiving any assistance. The limitations of SmartLP are indicated and the focus of further research is proposed. Keywords: Case-based system, CBR approach, knowledge acquisition, knowledge representation, case representation, evaluation, lesson planning

A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers

The Role of Geospatial Thinking and Geographic Skills in Effective Problem Solving with GIS: K-16 Education

Effective use of a Geographic Information System (GIS) is hampered by the limited geospatial reasoning abilities of students. The ability to reason with spatial relations, more specifically apply geospatial concepts, including the identification of spatial patterns and spatial associations, is important to geographic problem solving in a GIS context. This dissertation examines the broad influence of three factors on GIS problem solving: 1) affection towards computers, geography, and mathematics, 2) geospatial thinking, as well as 3) geographic skills. The research was conducted with 104 students in Waterloo, Ontario, Canada. Students were drawn from four educational levels: grade 9 students, 13 to 14 years of age; 1st year undergraduate university students, 3rd and 4th year undergraduate geography majors; and geography students at the graduate level ranging from 22 to 32 years of age. The level of affection is measured with modified scales borrowed from psychology. Results show that students in general exhibit positive sentiments toward computers and geography but less so towards mathematics. Spatial thinking and knowledge of geospatial concepts are measured by a 30-item scale differentiating among spatial thinkers along a novice-expert continuum. Scores on the scale showed an increase in spatial reasoning ability with age, grade, and level of education, such that grade 9 students averaged 7.5 out of 30 while the mean score of graduate students was 20.6. The final exercise assessed pertinent skills to geography namely inquiry, data collection, and analysis. In general, there was a positive correlation in the scores such that the skill proficiency increased with grade. Related analysis found three factors that affect problem-solving performance with a GIS. These include age, geographic skills (inquiry and analysis), and geospatial thinking (subscales analysis, representation, comprehension, and application). As well, the relationship(s) between performance on the geospatial scale and the observed problem-solving sequences and strategies applied on a GIS was examined. In general, students with lower scores were more apt to use basic visualization (zoom/measure tools) or buffer operations, while those with higher scores used a combination of buffers, intersection, and spatial queries. There were, however, exceptions as some advanced students used strategies that overly complicated the problem while others used visualization tools alone. The study concludes with a discussion on future research directions, followed by a series of pencil and paper games aimed to develop spatial thinking within a geographic setting