Learning Analytics: Challenges and Future Research Directions

In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.

Abstrahierendes Lernen durch aktive Modellbildung: Evaluation eines Prozesses und einer Lernumgebung

Die Fähigkeit zum Lernen durch Abstraktion aus Erfahrungen unterscheidet Experten von Novizen. Wir stellen einen Prozess für individuelles abstrahierendes Lernen und eine diesen Prozess unterstützende Lernumgebung vor. Die Ergebnisse einer Pilotstudie zeigen, dass Lernende unter Nutzung der Lernumgebung aus Fallbeispielen ein abstraktes Modell erstellen und über ihren Prozess reflektieren konnten. Dies fiel ihnen leichter, wenn die Fallbeispiele wenige gemeinsame Oberflächenmerkmale aufwiesen. Im Gegensatz zum intendierten Lernprozess wandten manche Lernende einen anderen Prozess an.

Choosing and Maintaining Programs for Sex Education in Schools: The CHAMPSS Model

Background: Despite effective solutions to reduce teen birth rates, Texas teen birth rates are among the highest in the nation. School districts can impact youth sexual behavior through implementation of evidence-based programs (EBPs); however, teen pregnancy prevention is a complex and controversial issue for school districts. Subsequently, very few districts in Texas implement EBPs for pregnancy prevention. Additionally, school districts receive little guidance on the process for finding, adopting, and implementing EBPs. Purpose: The purpose of this report is to present the CHoosing And Maintaining Programs for Sex education in Schools (CHAMPSS) Model, a practical and realistic framework to help districts find, adopt, and implement EBPs. Methods: Model development occurred in four phases using the core processes of Intervention Mapping: 1) knowledge acquisition, 2) knowledge engineering, 3) model representation, and 4) knowledge development. Results: The CHAMPSS Model provides seven steps, tailored for school-based settings, which encompass phases of assessment, preparation, implementation, and maintenance: Prioritize, Asses, Select, Approve, Prepare, Implement, and Maintain. Advocacy and eliciting support for adolescent sexual health are also core elements of the model. Conclusion: This systematic framework may help schools increase adoption, implementation, and maintenance for EBPs.

Semantic Technology Recommendation Based on the Analytic Network Process

Semantic technologies have become widely adopted in recent years, and choosing the right technologies for the problems that users face is often a difficult task. This paper presents an application of the Analytic Network Process for the recommendation of semantic technologies, which is based on a quality model for semantic technologies. Instead of relying on expert-based comparisons of alternatives, the comparisons in our framework depend on real evaluation results. Furthermore, the recommendations in our framework derive from user quality requirements, which leads to better recommendations tailored to users’ needs. This paper also presents an algorithm for pairwise comparisons, which is based on user quality requirements and evaluation results.

An Intelligent Assistant for Public Transport Management

This paper describes the architecture of a computer system conceived as an intelligent assistant for public transport management. The goal of the system is to help operators of a control center in making strategic decisions about how to solve problems of a fleet of buses in an urban network. The system uses artificial intelligence techniques to simulate the decision processes. In particular, a complex knowledge model has been designed by using advanced knowledge engineering methods that integrates three main tasks: diagnosis, prediction and planning. Finally, the paper describes two particular applications developed following this architecture for the cities of Torino (Italy) and Vitoria (Spain).

Validating Ontologies with OOPS!

Ontology quality can be affected by the difficulties involved in on-tology modelling which may imply the appearance of anomalies in ontologies. This situation leads to the need of validating ontologies, that is, assessing their quality and correctness. Ontology validation is a key activity in different ontol-ogy engineering scenarios such as development and selection. This paper con-tributes to the ontology validation activity by proposing a web-based tool, called OOPS!, independent of any ontology development environment, for de-tecting anomalies in ontologies. This tool will help developers to improve on-tology quality by automatically detecting potential errors.

A survey of agent-oriented methodologies

This article introduces the current agent-oriented methodologies. It discusses what approaches have been followed (mainly extending existing object oriented and knowledge engineering methodologies), the suitability of these approaches for agent modelling, and some conclusions drawn from the survey.

Analysis and design of multiagent systems using MAS-CommonKADS

This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network.

knOWLearn: a reuse-based approach for building ontologies in a semi-automatic way

In this poster paper we present an overview of knOWLearn, a novel approach for building domain ontologies in a semi-automatic fashion.

Verifying ontology requirements with SWIP

Verifying whether an ontology meets the set of established requirements is a crucial activity in ontology engineering. In this sense, methods and tools are needed (a) to transform (semi-)automatically functional ontology requirements into SPARQL queries, which can serve as unit tests to verify the ontology, and (b) to check whether the ontology fulfils the requirements. Thus, our purpose in this poster paper is to apply the SWIP approach to verify whether an ontology satisfies the set of established requirements.

Benchmarking for stability evaluation of computer systems

In this paper we propose a new benchmark to drive making decisions in maintenance of computer systems. This benchmark is made from load average sample data. The main goal is to improve reliability and performance of a set of devices or components. In particular, the stability of the system is measured in terms of variability of the load. A forecast of the behavior of this stability is also proposal as part of the reporting benchmark. At the final stage, a more stable system is obtained and its global reliability and performance can be then evaluated by means of appropriate specifications.

Risk assessment of the spanish national railway system

The principal risks in the railway industry are mainly associated with collisions, derailments and level crossing accidents. An understanding of the nature of previous accidents on the railway network is required to identify potential causes and develop safety systems and deploy safety procedures. Risk assessment is a process for determining the risk magnitude to assist with decision-making. We propose a three-step methodology to predict the mean number of fatalities in railway accidents. The first is to predict the mean number of accidents by analyzing generalized linear models and selecting the one that best fits to the available historical data on the basis of goodness-offit statistics. The second is to compute the mean number of fatalities per accident and the third is to estimate the mean number of fatalities. The methodology is illustrated on the Spanish railway system. Statistical models accounting for annual and grouped data for the 1992-2009 time period have been analyzed. After identifying the models for broad and narrow gauges, we predicted mean number of accidents and the number of fatalities for the 2010-18 time period.

Preference intensity in MCDM when an additive utility function represents DM preferences

We propose a new method for ranking alternatives in multicriteria decision-making problems when there is imprecision concerning the alternative performances, component utility functions and weights. We assume decision maker?s preferences are represented by an additive multiattribute utility function, in which weights can be modeled by independent normal variables, fuzzy numbers, value intervals or by an ordinal relation. The approaches are based on dominance measures or exploring the weight space in order to describe which ratings would make each alternative the preferred one. On the one hand, the approaches based on dominance measures compute the minimum utility difference among pairs of alternatives. Then, they compute a measure by which to rank the alternatives. On the other hand, the approaches based on exploring the weight space compute confidence factors describing the reliability of the analysis. These methods are compared using Monte Carlo simulation.

Software as a service: Undo

This paper proposes a highly automated mechanism to build an undo facility into a new or existing system easily. Our proposal is based on the observation that for a large set of operators it is not necessary to store in-memory object states or executed system commands to undo an action; the storage of input data is instead enough. This strategy simplifies greatly the design of the undo process and encapsulates most of the functionalities required in a framework structure similar to the many object-oriented programming frameworks.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.