Cognitive load and its importance in instructional design
To understand, accommodate and align the interaction between learners’ cognitive system and the given learning environment, the cognitive load theory (CLT) has become an acknowledged and broadly applied theory for instruction and learning (Van Merriënboer & Sweller, 2005; Schnotz & Kürschner, 2007).
Cognitive load theory is a framework of instructional design principles based on the characteristics and relations between the structures that constitute human cognitive architecture, particularly working memory and long-term memory (Wong, Leahy, Marcus, & Sweller, 2012).
1- For a multimedia instructional design,
CLT responses the limited working memory for holding visual (such as figures) and verbal (such as text) information as well as the number of operations it can perform on the information (Van Gerven & Pascal, 2003).
Cognitive load is defined as a multidimensional construct representing the load that a particular task imposes on the performer (Paas & van Merrienboer, 1994). It can be assessed by measuring mental load, mental effort (Sweller, van Merriënboer, & Paas 1998; Paas, Tuovinen,
Tabbers, & Gerven, 2003). Mental effort is related to the strategies used in the learning activities, whereas mental load refers to the interactions between the learning tasks, subject characteristics and subject materials, which are highly related to the complexity of the learning content that the students need to face (Hwang & Chang, 2011).
2- To respond to the reality that most digital
learning materials are developed with multimedia, Mayer (2001) proposed a cognitive theory of multimedia learning (CLML), which assumes that human process pictorial and verbal materials via different sense channels (i.e., sight and hearing).
Consequently, cognitive overloading could occur when learners receive redundant information, poorly structured information, or large amount of information in a sense channel. On the other hand, Paas, Tuovinen, Merriënboer and Darabi (2005) addressed that learners’ motivation had a significant relation with cognitive load, especially on mental effort.
They suggested that motivation could be identified as a dimension that determines learning success, especially in complex e-learning environments (Paas, Tuovinen, Merriënboer and Darabi, 2005). The relationship between cognitive load and motivation is also stated by Moos (2009)
3- Adaptive learning systems
An adaptive learning system aims to provide a personalized learning resource for students, especially learning content and user-preferred interfaces for processing their learning (Aroyo et al., 2006). Brusilovsky (2001) has indicated that two adaptation approaches can be used in developing web-based adaptive learning systems, that is, "adaptive presentation"
which presents personalized content for individual students, and "adaptive navigation support" which guides individuals to find the learning content by suggesting personalized learning paths. Other researchers have further indicated the importance of providing personalized user interfaces to meet the learning habits of students (Mampadi, Chen,
4- Ghinea, & Chen, 2011). 187
In the past decade, various adaptive learning systems have been developed based on different parameters that represent the characteristics or preferences of students as well as the attributes of learning content (Wang & Wu, 2011). For example,
Karampiperis and Sampson (2005) proposed an adaptive resource selection scheme by generating all of the candidate learning paths that matched the learning objectives and then selecting the most fitting one based on the suitability of the learning resources for individual students. Hwang, Kuo,
5 - Yin and Chuang (2010) further developed
An adaptive learning system to guide individuals to learn in a real-world environment by generating the personalized learning paths based on the learning status of each student and the relationships between the authentic learning targets.
It can be seen that the provision of personalization or adaptation modules, including personalized learning materials, navigation paths or user interfaces, has been recognized as an important issue for developing effective learning systems (Chiou, Tseng, Hwang, & Heller, 2010; van Seters, Ossevoort, Tramper, & Goedhart, 2012). Several studies have been conducted to develop adaptive learning systems based on learning styles or cognitive styles.
6- For example, Tseng, Chu, Hwang and
Tsai (2008) proposed an adaptive learning system for elementary school mathematics courses by considering students' learning styles and the difficulty of the learning content. Mampadi, Chen, Ghinea and Chen (2011) developed a web-based learning environment by providing different user interfaces based on students' cognitive styles.
Furthermore, Hsieh, Jang, Hwang and Chen (2011) developed an adaptive mobile learning system that guided individual students to learn in a butterfly ecology garden based on students' learning styles. However, few studies have considered multiple learning criteria, including learning styles, cognitive styles, and knowledge levels, for developing adaptive learning systems.
7- Research questions
In this study, an adaptive learning system is developed based by taking both cognitive styles and learning styles into account. It is expected that the proposed approach can benefit students in improving their learning achievement, reducing their cognitive load and promoting their learning motivation. Accordingly, the following research questions are investigated:
1. Does the adaptive learning system developed based on both cognitive styles and learning styles benefit students more than the conventional learning style-based system in terms of learning achievements?
2. Can the learning system developed based on both cognitive styles and learning styles decrease students’ cognitive load in comparisons with the conventional learning style-based system?
3. Does the learning system developed based on both cognitive styles and learning styles benefit students more than conventional learning style-based system in terms of learning motivations?
Adaptive learning system with multi-dimensional personalization criteria In this section, an adaptive
learning system, AMDPC (Adaptation with Multi-Dimensional Personalization Criteria) is presented. AMDPC consists of four modules: the Learning content-Generating Module (LCGM), the Adaptive Presentation Module (APM), the Adaptive Content Module (ACM) and the Learning Module (LM).
7- Learning content-generating module
Figure 1 presents the concept of the learning content-generating module, which is used to extract contents from raw materials and generate chunks of information for composing personalized learning materials based on the presentation layout. Each subject unit contains a set of components, such as the ID of the unit, texts, photos, etc. The components of a subject unit are classified into the following six categories:
• Concept unit: containing the title, concept ID, abstract and representative icon of the course unit.
• Text components: the text content of the course unit.
• Example component: the illustrative examples related to the course content.
• Figure component: the pictures, photos and figures related to the course unit.
• Fundamental component: Fundamental components contain the primary contents of a course, including the title of each learning unit or concept, and the corresponding texts, figures, examples and exercises.
• Supplementary component: Supplementary components contain supplementary materials that are helpful to students in extending the learning scope or realizing the concepts to be learned.
After selecting the appropriate components (learning materials), LCGM organizes the selected components based on individual students' learning styles and cognitive styles. The organized learning content is then presented to individual students based on the presentation layout framework. Figure 2 shows this framework, which consists of the following areas: