Development and Use of an Adaptive Learning Environment to Research Online Study Behaviour

By Jonsdottir, Anna Helga; Jakobsdottir, Audbjorg et al. | Educational Technology & Society, January 2015 | Go to article overview

Development and Use of an Adaptive Learning Environment to Research Online Study Behaviour


Jonsdottir, Anna Helga, Jakobsdottir, Audbjorg, Stefansson, Gunnar, Educational Technology & Society


Introduction

With the increasing number of web-based educational systems and learning environments several types of systems have emerged. These include the learning management system (LMS), learning content management system (LCMS), virtual learning environment (VLE), course management system (CMS) and Adaptive and intelligent web-based educational systems (AIWBES). The terms VLE and CMS are often used interchangeably, CMS being more common in the United States and VLE in Europe.

The LMS is designed for planning, delivering and managing learning events, usually adding little value to the learning process nor supporting internal content processes (Ismail, 2001). A VLE provides similar service, adding interaction with users and access to a wider range of resources (Piccoli, Ahmad & Ives, 2001). The primary role of a LCMS is to provide a collaborative authoring environment for creating and maintaining learning content (Ismail, 2001). Classes taught on these platforms are accessible through a web-browser but are usually private, i.e., only individuals who are registered for a class have access to the password-protected website.

A number of content providers can be found on the web. Even though they are not educational systems per se, linking them to learning systems would make the content available to a larger audience and save work on creating material within the learning systems. Examples of existing content providers are Khan Academy and Connexions. A number of academic institutions have also made educational material available, including MIT OpenCourseWare and Stanford Engineering Everywhere.

Many systems are merely a network of static hypertext pages (Brusilovsky, 1999) but adaptive and intelligent web-based educational systems (AIWBES) use a model of each student to adapt to the needs of that student (Brusilovsky & Peylo, 2003). These systems tend to be subject-specific because of their structural complexity and therefore do not provide a broad range of content. The first AIWBES systems were developed in the 1990s. These include ELM-ART (Brusilovsky, Schwartz & Weber, 1996; Weber & Brusilovsky, 2001) and the AHA! system (De Bra & Calvi, 1998). Since then, many interesting systems have been developed, many of which focus on a specific subject, often within computer science. Examples of AIWBES systems used in computer science education are SQL-Tutor (Mitrovic, 2003), ALEA (Bielikova, 2006), QuizGuide (Brusilovsky & Sosnovsky, 2005; Brusilovsky, Sosnovsky & Shcherbinina, 2004) and Flip (Barla et al., 2010) which includes an interesting way of allocating quiz questions to students (discussed further in the following section). AIWBES systems can be found in other fields such language teaching (Chen, Lee & Chen, 2005; Heift & Nicholson, 2001) and teaching modelling of dynamic systems (Zhang et al., 2014) to name some. Systems including competitive web-based drill games are also available, with an overview presented in Gonzalez-Tablas, Fuentes, Hernandez-Ardieta and Ramos (2013).

The goal of the project described here is to build an AIWBES including the functionalities of an LCMS. The system should be open to everyone having access to the web and provide broad educational content including interactive drills with the primary purpose of enhancing learning. Intelligent methods will be used for item allocation in drills and for directing students toward appropriate material. As discussed in Romero and Ventura (2007), great possibilities lie in the use of educational datamining. The behaviour of the students in the system are logged so the system provides a testbed for research on web-assisted education such as drill item selection methods.

It has been described earlier how students tend to strive for higher grades in similar systems (Stefansson, 2004). The present paper considers these drivers more explicitly, namely how the student behaviour, including stopping times, reflects their achievements and likely immediate performance, as predicated by system design. …

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