El Estado, las primeras instituciones y el agua

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Intelligent assessment consists of: assessment scenarios, AI, and the use of a cognitively- grounded measurement model [82]. The assessment scenario consists of context specific tasks, the performance of which will be assessed by automated routines that emulate the behaviour of an expert (the model-based measurement tool, based on AI technique), to

provide an assessment outcome that impact instructionally useful information [82]. Educational assessment has been subject to numerous studies. The emergence of Bayesian reasoning AI technique as a powerful probabilistic inference tool catalyzed research on intelligent assessment in the educational context. [83] presented a preliminary study of the progress of research on educational assessment using BNs, highlighting the different areas of application of BN-based models, in the educational assessment context. [84] reported the construction of a model for estimating an educational institution’s performance, with respect to a specific subject. The aim was to develop a diagnostic model of an institution’s effectiveness or quality. They identified and modelled the causal relationship between factors affecting the effectiveness of an educational system, such as instructors' knowledge and experience, class size, student and instructor attendance, among others. [85] presented a model for evaluating students learning styles based on the Index of Learning Styles by [86]. Data recorded from the observation of a student's learning behaviour in web-based courses are used to automatically detect the student's learning style. [87] presented a model for assessing students' grasp of a particular concept. The model assesses students’ current state in an open/distance informatics course based on such factors as: use of computers, web-based material, textbook, and internet, family status, and hours of work. The model is aimed at predicting whether a student is likely to dropout or continue with a course.

Other related works that demonstrate the feasibility and benefits of performance assessment of students learning in the virtual learning environment (VLE), using BN AI technique, are hereby cited. The design of a BN-based model for the prediction of students’ performance in a test and the application of a BN-based model in educational computerized adaptive testing to assess students’ performance were reported by [88]and [89], respectively. [90] applied BN in adaptive testing of multiple student latent traits in a single item-based test, using granularity hierarchies. Bayesian inference is used to propagate knowledge over the hierarchies. Each trait is either mastered or not mastered. [91] presented a BN- and rule-based model for the assessment of students’ learning, using students’ knowledge map and analysis of their responses to tests items. The model was designed for the assessment of students’ software usage abilities and skills in a computer course. [92] reported a BN-based model of student activity performance for assessing students’ learning processes in a VLE, from the analysis of the students’ web portfolios. Assessment is based on such variables as self reflection, homework, read frequency, login frequency, and peer interactions.

These intelligent assessment models, though BN-based, are not aimed at students’ laboratory work performance assessment. That is, literature search has highlighted previous work on performance assessment in virtual learning environments but has not identified performance- based assessment in the context of virtual laboratories generally and virtual electronic laboratories specifically. Furthermore, most of these existing BN-based assessment tools, though designed for VLEs, are focused mostly on the ITS/ATS environment, the common and initial area of application of intelligent assessment models. ITS/ATS are software tools for teaching, aimed at achieving a one-to-one tutoring process, without the instructor. The output of the assessment model, in an ITS/ATS, is often used to determine what instructional material or assessment item to give the learner next or what concept to teach next [93]. Also, some of the existing models are focused on the assessment of such constructs as learning style, effectiveness, quality, grasp of concept, and learning, while others are focused on item- based tests assessment.

On-Line Assessment of Expertise (OLAE) presented by [93] and [94] is another BN-based performance assessment model designed for the assessment of students’ knowledge of Newtonian mechanics in a VLE. OLAE maintains a list of the correct and incorrect rules for solving problems in the domain, against which it matches a student’s problem solving solution steps, using a BN and a solution graph, in order to assess the set of rules that have been mastered by a student. OLAE does not assess students’ abilities to undertake laboratory activities or analyze experimental results [93]. Also, it “does not monitor laboratory tasks, large-scale projects or their [students’] hands-on activities” [95]. The BN-based assessment model by [96] designed for a VLE consisting of an ITS and a robotics virtual laboratory, was used in the ITS component of the VLE to assess students’ knowledge of course themes.

A BN-based assessment tool, which is part of the work presented in this thesis, was constructed for the performance assessment of students’ laboratory work in the VEL environment. The next section describes Bayesian networks and their underlying theory and associated concepts, highlighting the advantages of BNs over other AI techniques.