Learning Objects and Generative Learning for Higher Order Thinking

Learning Objects and Generative Learning for Higher Order Thinking

Tan Wee Chuen, Baharuddin Aris, Mohd Salleh Abu
DOI: 10.4018/978-1-59904-861-1.ch034
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Abstract

This chapter aims to guide the readers through the design and development of a prototype Web-based learning system based on the integration of learning objects with the principles of generative learning to improve higher order thinking skills. The chapter describes the conceptual model called Generative Learning Object Organizer and Thinking Tasks (GLOOTT) which was used to design and build a technology- supported learning environment. The chapter then describes how the effectiveness of the Web-based learning system was evaluated and reflects on the importance of the findings more generally.
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Background

The emergence of the World Wide Web has caused change and innovation in the way people learn and work. An educational innovation is gradually taking place in the development and delivery of instruction through the use of learning objects. The changes provide an opportunity to improve the learning with the appropriate use of pedagogy coupled with technologies.

Most instructional designers understand the importance of pedagogical perspectives in the design and development of Web learning environments. Snow (1989) noted that instruction differs in structure and completeness, and highly structured instruction (linear in sequence with restricted and high external control) seems to help learners with low ability but hinder those with high ability. This suggests that the concept of one-size-fits-all design is not suitable in the design and development in e-learning. Instead, the learning environment should be highly flexible in structure and transfer the control of the learning system from the instructors to the learners whereby learners can actively participate in the learning process. The concept of learning object design fits this goal very well as can provide flexible paths for the learners’ exploration. Nonlinearity in the learning object approach allows students to access information in different patterns and to take control in their own actions and learning.

Learning object has been described by Wiley (2000) as reusable digital resource that supports learning. Grounded in the object-oriented paradigm of computer science, learning objects require the design of instruction into small learning contents that can be reused in different contexts, deployed into multiple setting and learning goals (Collis & Strijker, 2003; Wiley, 2000).

The idea of packaging information in small, reusable, and flexible units in a learning environment has received a lot of attention from the educators and instructional designers of e-learning environments. According to Reigeluth and Nelson (1997), when teachers first gain access to instructional materials, they often break the materials down into their constituting parts and then reassemble these parts in ways that support their instructional goals. Thus, the notion of small and reusable units of learning content, learning components, and learning object design have the potential to provide flexibility and reusability by simplifying the assembly and disassembly of instructional design and development.

Key Terms in this Chapter

Thinking Tasks: Thinking tasks in this chapter are the scenario-based problems that involve learners with HOTS. Thinking tasks aim to test their understandings as well as to reinforce and practice HOTS as mentioned by Costa and Kallick (2001). There are two parts in thinking tasks, namely Try It Out and Apply It. Try It Out contains multiple-choice questions uploaded by the instructor to assess the students’ understanding. Apply It consists of scenario-based problems that engage learners with HOTS.

Learning Object: A learning object is a self-contained, flexible, and reusable chunk of instruction that can be assembled with other objects to facilitate the learning.

Lesson Mapping: Lesson mapping is the concept mapping in GOOD learning system. It is the generative learning activity designed in the Web-based learning system that aims to engage students in HOTS. It is an outline form of concept map suggested by Alpert and Grueneberg (2000) and Dabbagh (2001).

GLOOTT Model: GLOOTT refers to Generative Learning Object and Thinking Tasks. It is a pedagogically-enriched conceptual model that consists of learning object, generative learning, and HOTS.

Generative Learning: Generative learning is a type of instruction developed by constructivists. The generative learning activities involve the creation of relationships and meanings of the learning. In the generative learning, students are active in the knowledge construction. Experts and researchers advocate that concept mapping and problem solving are activities of generative learning. Concept mapping and problem solving will engage students in analysis, synthesis, and evaluation skills. Thus, it is important to integrate these skills into learning in order to promote HOTS. In the generative learning environment, students are active in constructing meaningful understanding of information found and generating relationships among the information.

HOTS: HOTS is the abbreviation of Higher Order Thinking Skills. The cognitive operations of HOTS are Analysis, Synthesis, and Evaluation (Bloom et al., 1956; Bloom et al., 1971). Table 4 describes the features of the Bloom Taxonomy of Thinking used in this research (Bloom et al., 1956).

Reflection Corner: According to Fogarty (2002), reflective thinking is the foundation of HOTS. Students are self-aware and they plan, monitor, and evaluate their thinking and learning when they engage in reflective thinking. Thus, Reflection Corner acts as a self-assessment tool to help the learners in monitoring their engagement with HOTS and reflecting their learning. It consists of a checklist, called “How am I doing” checklist to enable learners to reflect their engagement with HOTS.

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