Abstract

Abstract

Abstract
Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

Abstract
The ability to have an automated real time detection of user interest during a web session is very appealing and can be very useful for a number of web intelligence applications. Low level interaction events associated with user interest manifestations form the basis of user interest models. However such data sets present a number of challenges from a machine learning perspective, including the level of noise in the data and class imbalance (given that the majority of content will not be of interest to a user). In this paper we evaluate a large number of machine learning techniques aimed at learning from class imbalanced data using two data sets collected from a real user study. We use the AUC, recall, precision and model complexity to compare the relative merits of these techniques and conclude that useful models with AUC above 0.8 can be obtained using a mix of sampling and cost based methods. Ensemble models can provide further accuracy but make deployment more complex.

Abstract
Currently, the teaching-learning process in domains, such as computer programming, is characterized by an extensive curricula and a high enrolment of students. This poses a great workload for faculty and teaching assistants responsible for the creation, delivery, and assessment of student exercises. The main goal of this chapter is to foster practice-based learning in complex domains. This objective is attained with an e-learning framework-called Ensemble-as a conceptual tool to organize and facilitate technical interoperability among services. The Ensemble framework is used on a specific domain: computer programming. Content issues are tacked with a standard format to describe programming exercises as learning objects. Communication is achieved with the extension of existing specifications for the interoperation with several systems typically found in an e-learning environment. In order to evaluate the acceptability of the proposed solution, an Ensemble instance was validated on a classroom experiment with encouraging results. © 2015, IGI Global.

Abstract
The research presented in this paper is part of an ongoing work to define semantic relatedness measures to any given semantic graph. These measures are based on a prior definition of a family of proximity algorithms that computes the semantic relatedness between pairs of concepts, and are parametrized by a semantic graph and a set of weighted properties. The distinctive feature of the proximity algorithms is that they consider all paths connecting two concepts in the semantic graph. These parameters must be tuned in order to maximize the quality of the semantic measure against a benchmark data set. From a previous work, the process of tuning the weight assignment is already developed and relies on a genetic algorithm. The weight tuning process, using all the properties in the semantic graph, was validated using WordNet 2.0 and the data set WordSim-353. The quality of the obtained semantic measure is better than those in the literature. However, this approach did not produce equally good results in larger semantic graphs such as WordNet 3.0, DBPedia and Freebase. This was in part due to the size of these graphs. The current approach is to select a sub-graph of the original semantic graph, small enough to enable processing and large enough to include all the relevant paths. This paper provides an overview of the ongoing work and presents a strategy to overcome the challenges raise by large semantic graphs.