6.4: Design Principles for Developing Learning Materials for Emerging Technologies

In developing learning materials for any technology, learning theories must be used for effective and efficient instruction. This section will address theories and design principles for emerging technologies.

Early learning materials development was influenced by behaviourist learning theory. Behaviourists claim that it is the observable behaviour of the learner that indicates whether or not they have learned, not what is going on in the learner’s head. Early instructional methods, such as the teaching machine, were influenced by behaviourist theory. The teaching machine taught by drill and practice, and transferred the repetitiveness of teaching from the instructors to the machine.

Cognitive learning theory influenced the development of learning materials with the introduction of computer-based instruction. Cognitive psychologists see learning as a process involving the use of memory, motivation, and thinking, and that reflection plays an important part in learning. Cognitivists perceive learning as an internal process and claim that the amount learned depends on the processing capacity of the learner, the amount of effort expended during the learning process, the quality of the processing, and the learner’s existing knowledge structure. Cognitive theory was influenced by information processing theory, which proposes that learners use different types of memory during learning.

As technology emerged, there was more emphasis on learner-centred education, which promoted the use of constructivist theory in the development of learning materials. Constructivists claimed that learners interpret information and the world according to their personal reality, and that they learn by observation, processing, and interpretation, and then personalize the information into their own worldview. Also, learners learn best when they can contextualize what they learn for immediate application and to acquire personal meaning. The learner-centred approach allows learners to develop problem-solving skills and learn by doing rather than by being told.

They are many instructional design models for developing learning materials. Dick et al. (2001) proposed a design model with the major components being design, development, implementation, and evaluation of instruction. Another widely used model is by Gagné et al. (1991) who claimed that strategies for learning should be based on learning outcomes. Gagné specifies nine types of instructional events:

• gain the learner’s attention;
• inform the learner of the lesson objectives;
• stimulate recall of prior knowledge;
• present stimuli with distinctive features to aid in perception;
• guide learning to promote semantic encoding;
• elicit performance;
• provide informative feedback;
• assess performance; and
• enhance retention and learning transfer.

Most of the current and past instructional design models were developed for classroom and print-based instruction rather than for learner-centred instruction and e-learning. New instructional design models are needed to develop learning materials for delivery on emerging technologies.

According to Jacobs and Dempsey (2002), some emerging influences that will affect future instructional design include object-oriented distributed learning environments, the use of artificial intelligence techniques, cognitive science, and neuroscience. Below are guidelines for educators to develop learning materials for delivery via emerging technologies.

Tips and Guidelines

• Information should be developed in “chunks” to facilitate processing in working memory since humans have limited working memory capacity. Chunking is important for mobile technologies that have small display screens, such as cell phones, PDAs, etc.
• Content should be broken down into learning objects to allow learners to access segments of learning materials to meet their learning needs. A learning object is defined as any digital resource that can be re-used to achieve a specific learning outcome (Ally, 2004b). Learning materials for emerging technologies should be developed in the form of information objects, which are then assembled to form learning objects for lessons. A learning session using a mobile device can be seen as consisting of a number of information objects sequenced in a pre-determined way, or sequenced, according to the user’s needs. The learning object approach is helpful where learners will access learning materials just in time, as they complete projects using a self-directed, experiential approach. Also, learning objects allow for instant assembly of learning materials by learners and by intelligent software agents to meet learners’ needs.
• Use the constructivist approach to learning to allow learners to explore and personalize the materials during the learning process. Learning should be project-based to allow learners to experience the world by doing things, rather than passively receiving information, to build things, to think critically, and to develop problem-solving skills (Page, 2006). Mobile technologies facilitate project-based learning since learners can learn in their own time and in their own environments. For example, as learners complete a project they can use wireless mobile technology to access just in time information and the instructor as needed.
• Simple interfaces prevent cognitive overload. For example, graphic outlines can be used as interfaces and as navigational tools for learners. The interface should allow the learner to access learning materials with minimal effort and navigate with ease. This is critical for emerging technologies since some output devices are small.
• Use active learning strategies that allow learners to summarize what they learn and to develop critical thinking skills. For example, learners can be asked to generate a concept map to summarize what they learned. A concept map or a network diagram can show the important concepts in a lesson and the relationship between them. Learner-generated concept maps allow learners to process information at a high level. High-level concept maps and networks can represent information spatially, so learners can see the main ideas and their relationships.
• Learning materials should be presented so that information can be transferred from the senses to the sensory store, and then to working memory. The amount of information transferred to working memory depends on the importance assigned to the incoming information and whether existing cognitive structures can make sense of the information. Strategies that check whether learners have the appropriate existing cognitive structures to process the information should be used in emerging technologies delivery. Pre-instructional strategies, such as advance organizers and overviews, should be used if relevant cognitive structures do not exist.
• There should be a variety of learning strategies to accommodate individual differences. Different learners will perceive, interact with, and respond to the learning environment in different ways, based on their learning styles (Kolb, 1984).

According to Kolb, there are four learning style types:

1. Divergers are learners who have good people skills. When working in groups, they try to cultivate harmony to assure that everyone works together smoothly.
2. Assimilators like to work with details, and are reflective and relatively passive during the learning process.
3. Convergers prefer to experiment with, and apply new knowledge and skills, often by trial and error.
4. Accommodators are risk-takers, who want to apply immediately what they learn to real-life problems or situations.

Examples of strategies to cater for individual learning preferences include:

• Use visuals at the start of a lesson to present the big picture, before going into the details of the information.
• For the active learners, strategies should provide the opportunity to immediately apply the knowledge.
• To encourage creativity, there must be opportunities to apply what was learned in real-life situations so that learners can go beyond what was presented.
• The use of emerging technologies will make it easier to cater to learners’ individual differences by determining preferences, and using the appropriate learning strategy based on those preferences.
• Provide learners the opportunity to use their meta-cognitive skills during the learning process. Meta-cognition is a learner’s ability to be aware of their cognitive capabilities and to use these capabilities to learn. This is critical in e-learning, since learners will complete the learning materials individually. Exercises with feedback throughout a lesson are good strategies to allow learners to check their progress, and to adjust their learning approach as necessary.
• Learners should be allowed to construct knowledge, rather than passively receive knowledge through instruction. Constructivists view learning as the result of mental construction where learners learn by integrating new information with what they already know.
• Learners should be given the opportunity to reflect on what they are learning and to internalize the information. There should be embedded questions throughout the learning session to encourage learners to reflect on, and process the information in a relevant and meaningful manner. Learners can be asked to generate a journal to encourage reflection and processing. Interactive learning promotes higher-level learning and social presence, and personal meaning (Heinich et al., 2002).

Intelligent agents should be embedded in the technology to design instruction and deliver the instruction based on individual learner needs. An intelligent agent gathers information about learners and them respond based on the what was learned about the student. For example, if a learner consistently gets a question on a concept wrong, the intelligent agent will prescribe other learning strategies until the learner master the concept. As the user interacts with the system, the agent learns more about the learner. This is critical, as learning materials may be accessed by people globally. These agents can be proactive so that they can recognize and react to changes in the learning process. As the intelligent agent interacts with the learner, it gains more experience by learning about the learner and then determining the difficulty of materials to present, the most appropriate learning strategy based on the learner’s style, and the sequence of the instruction (Ally, 2002). The intelligent learning system should reason about a learner’s knowledge, monitor progress, and adapt the teaching strategy to individual’s learning pattern (Woolf, 1987). For example, the intelligent system could monitor learning and build a best practice database for different learning styles. It could also track common errors and prescribe strategies to prevent similar errors in the future.