Mónica Trella

Bio: Mónica Trella is an academic researcher from University of Málaga. The author has contributed to research in topics: Computerized adaptive testing & Bayesian network. The author has an hindex of 10, co-authored 16 publications receiving 456 citations.

SIETTE: A Web-Based Tool for Adaptive Testing

Abstract: Student assessment is a very important issue in educational settings. The goal of this work is to develop a web-based tool to assist teachers and instructors in the assessment process. Our system is called SIETTE, and its theoretical bases are Computer Adaptive Testing and Item Response Theory. With SIETTE, teachers worldwide can define their tests, and their students can take these tests on-line. The tests are generated according to teachers' specifications and are adaptive, that is, the questions are selected intelligently to fit the student's level of knowledge. In this way, we obtain more accurate estimations of student's knowledge with significantly shorter tests. By using the computer, larger question databases can be stored, selection algorithms can be performed efficiently, and questions can include multimedia content. The use of Java applets allows the inclusion of executable content in question stem and/or answers, so the student can interact with the system by means of this applet. In this way, new possibilities are added to Computer Adaptive Tests, such as using traditional multiple-choice questions together with questions whose answer is evaluated by the applet itself.

Internet based Evaluation System

Abstract: In this paper, we describe the design and development of a web-based Computerized Adaptive Testing system (CAT) that is still under development and will be one of the main components of the TREE project. The TREE project consists in the development of a several web-based tools for the classification and identification of different European vegetable species (an expert system, interfaces for creating and updating databases and an intelligent tutoring system). The test generation system will be used by the ITS diagnostic module, and has a complete set of tools that not only assists teachers in test development and design, but also supports student evaluations. Adaptive capabilities are provided by an IRT model. While the student is taking the test, the system creates (and updates) his/her temporary student model. In this way, the system can be used in two different ways: as an independent evaluation tool over the WWW (SIETTE system, already finished), or as a component of the diagnostic module in any ITS with a curriculum structured knowledge base as the TREE ITS.

Introducing prerequisite relations in a multi-layered bayesian student model

Abstract: In this paper we present an extension of a previously developed generic student model based on Bayesian Networks. A new layer has been added to the model to include prerequisite relationships. The need of this new layer is motivated from different points of view: in practice, this kind of relationships are very common in any educational setting, but also their use allows for improving efficiency of both adaptation mechanisms and the inference process. The new prerequisite layer has been evaluated using two different experiments: the first experiment uses a small toy example to show how the BN can emulate human reasoning in this context, while the second experiment with simulated students suggests that prerequisite relationships can improve the efficiency of the diagnosis process by allowing increased accuracy or reductions in the test length.

The SIETTE Automatic Assessment Environment

Abstract: This article describes the evolution and current state of the domain-independent Siette assessment environment. Siette supports different assessment methods—including classical test theory, item response theory, and computer adaptive testing—and integrates them with multidimensional student models used by intelligent educational systems. Teachers can use an authoring tool to create large item pools of different types of questions, including multiple choice, open answer, generative questions, and complex tasks. Siette can be used for formative and summative assessment and incorporates different learning elements, including scaffolding features, such as hints, feedback, and misconceptions. It includes numerous other features covering different educational needs and techniques, such as spaced repetition, collaborative testing, or pervasive learning. Siette is designed as a web-based assessment component that can be semantically integrated with intelligent systems or with large LMSs, such as Moodle. This article reviews the evolution of the Siette system, presents information on its use, and analyses this information from a broader and critical perspective on the use of intelligent systems in education.

An Empirical Approach to On-Line Learning in SIETTE

Abstract: SIETTE is a web-based evaluation tool that implements CAT theory. With the help of a simulation program, different empirical experiments have been performed with SIETTE with two different goals: a) to study the influence of the parameters of characteristic item curves and selection criteria in test length and accuracy; and b) to study different learning strategies for these parameters. The results of the experiments are shown and interpreted.

User models for adaptive hypermedia and adaptive educational systems

Abstract: One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

ELM-ART: An Adaptive Versatile System for Web-based Instruction

Abstract: This paper discusses the problems of developing versatile adaptive and intelligent learning systems that can be used in the context of practical Web-based education. We argue that versatility is an important feature of successful Web-based education systems. We introduce ELM-ART, an intelligent interactive educational system to support learning programming in LISP. ELM-ART provides all learning material online in the form of an adaptive interactive textbook. Using a combination of an overlay model and an episodic student model, ELM-ART provides adaptive navigation support, course sequencing, individualized diagnosis of student solutions, and example-based problem-solving support. Results of an empirical study show different effects of these techniques on different types of users during the first lessons of the programming course. ELM-ART demonstrates how some interactive and adaptive educational components can be implemented in WWW context and how multiple components can be naturally integrated together in a single system.

Adaptive and Intelligent Technologies for Web-based Eduction.

Abstract: The paper provides a review of adaptive and intelligent technologies in a context of Web-based distance education. We analyze what kind of technologies are available right now, how easy they can be implemented on the Web, and what is the place of these technologies in large-scale Web-based education.

A review of recent advances in learner and skill modeling in intelligent learning environments

Abstract: In recent years, learner models have emerged from the research laboratory and research classrooms into the wider world. Learner models are now embedded in real world applications which can claim to have thousands, or even hundreds of thousands, of users. Probabilistic models for skill assessment are playing a key role in these advanced learning environments. In this paper, we review the learner models that have played the largest roles in the success of these learning environments, and also the latest advances in the modeling and assessment of learner skills. We conclude by discussing related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.

KnowledgeTree: a distributed architecture for adaptive e-learning

Abstract: This paper presents KnowledgeTree, an architecture for adaptive E-Learning based on distributed reusable intelligent learning activities. The goal of KnowledgeTree is to bridge the gap between the currently popular approach to Web-based education, which is centered on learning management systems vs. the powerful but underused technologies in intelligent tutoring and adaptive hypermedia. This integrative architecture attempts to address both the component-based assembly of adaptive systems and teacher-level reusability.