Modelling student online behaviour in a virtual learning environment

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) -- General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis -- and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities.Comment: In Proceedings of the 2014 Workshop on Learning Analytics and Machine Learning at the 2014 International Conference on Learning Analytics and Knowledge (LAK 2014

Modelling student online behaviour in a virtual learning environment

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) - General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis - and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities. Drahomira Herrmannova,Lucie Vachova,Jakub Kuzilek,Zdenek Zdrahal,Annika Wolf

Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user’s activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail

ITA 2.0: A Program for Classical and Inductive Item Tree Analysis

Item Tree Analysis (ITA) is an explorative method of data analysis which can be used to establish a hierarchical structure on a set of dichotomous items from a questionnaire or test. There are currently two different algorithms available to perform an ITA. We describe a computer program called ITA 2.0 which implements both of these algorithms. In addition we show with a concrete data set how the program can be used for the analysis of questionnaire data.

The Structural Relationship between Current and Capital Account Balance in India: A Time Series Analysis

The long run relationship between current account balance (CAB) and capital account balance (KAB) and the repercussions of capital account convertibility (KAC) on growth process of a country is a much debated issue. In particular, in the aftermath of the Southeast Asian crisis, the limitation of the liberal capital regime for a developing country like India is often highlighted in the literature. However, the probable impact of introducing KAC on CAB in India generally is discussed theoretically. Though some of the existing studies in India have earlier focused on this research question, they have done so by exogenously assuming the existence of a single structural break in the interrelationship between CAB and KAB. The present study intends to bridge the gap in the literature by raising two empirical questions: first, how far KAC is likely to destabilize the CAB and second, measuring the strength of the interrelationship between CAB and KAB. The current paper also contributes to the literature by incorporating multiple endogenous structural breaks in the empirical analysis. The empirical findings do not support any long term relationship between capital and current account balance and reveals that two significant structural breaks are observed in 1993-94 and 2003-04.International Capital Movements, Foreign Exchange, Current Account Adjustment

A logic approach for exceptions and anomalies in association rules

Association rules have been used for obtaining information hidden in a database. Recent researches have pointed out that simple associations are insu cient for representing the diverse kinds of knowledge collected in a database. The use of exceptions and anomalies deal with a di erent type of knowledge sometimes more useful than simple associations. Moreover ex- ceptions and anomalies provide a more comprehensive understanding of the information provided by a database. This work intends to go deeper in the logic model studied in [5]. In the model, association rules can be viewed as general relations between two or more attributes quanti ed by means of a convenient quanti er. Using this formulation we establish the true semantics of the distinct kinds of knowledge we can nd in the database hidden in the four folds of the contingency table. The model is also useful for providing some measures for assessing the validity of those kinds of rulesPeer Reviewe

Data Handling in Quantitative Microanalysis in Biology

In cell biology, electron probe X-ray microanalysis can reveal the distribution of chemical elements inside a single cell. The full description of a biological system (cell population, tissue) requires a great number of spot measurements. In quantitative analysis, the measurements are subject to experimental errors of several types; moreover, the relations between the resulting values are usually more interesting than the absolute concentrations. Nevertheless, the proper evaluation of quantitative values can discover information more on the object of study. A system of simple statistical tests is suggested here which can solve several problems. Some concentration values can be far from the statistical average due to errors in measurement; therefore, a statistical test of plausibility of the measured values is carried out. In the compartments (e.g., nucleus, cytoplasm or other selected areas), the distribution of an element can be nonhomogeneous, and hence a statistical test of homogeneity of the element distribution in specified areas is provided. The tests continue with a test for correlation, in which the concentrations of a given element in a pair of specified areas are compared. These tests proceed step-by-step for all elements of interest. Subsequently, the relations of concentrations in all possible pairs of elements in the area in question are calculated. Moreover, cells within a population can be different from the point of view of elemental concentration; a statistical test of homogeneity of the cell population is provided. In the case of nonhomogeneity, the concentration values and/or cells within a population are clustered into homogeneous groups. The evaluation is carried out automatically, with a simple program. The system of programs, in which the program for evaluation is incorporated, is included semi-on-line in the EDAX9900 system, where the measurement and evaluation are carried out in sequence. The results for a population of Srrepromyces aureofaciens are shown as an example

Briefly on the GUHA method of data mining, Journal of Telecommunications and Information Technology, 2003, nr 3

The paper gives brief, user-oriented, information on the GUHA method