# Cognition and Instruction/Technologies and Designs for Learning

In order to best use technology for teaching and learning, teachers and designers need to understand its potential benefits and pitfalls. This chapter examines theories about how cognitive processes are affected by multimedia learning environments and evidence-based principles for designing such environments. The first section introduces cognitive load theory and describes how the cognitive demands of a multimedia environment affect how students learn from it. The second section introduces the four component instructional design model which offers research-based guidance for designing materials and technologies to facilitate learning of complex skills. Finally, this chapter will look at how technology can be used to facilitate collaborative learning.

## Cognitive Load Theory

Cognitive load theory is an important aspect when looking at technology in the educational setting. Cognitive load theory is a theory proposed by John Sweller and focuses on working memory and instruction. Our working memory is only capable of processing a limited amount of information at one time When designing instructional tools working memory’s limitations is something that needs to be kept in mind, especially when factoring technology into instruction. The reason behind this is that if too much information is presented simultaneously working memory can become overloaded will either to fail to take in all of the information being presented or will shut down completely and take in none of the information. Sweller proposed that there are three types of cognitive load: intrinsic, extraneous, and germane. Through understanding the differences between these three types of cognitive load we should be able to analyze how multimedia presentations are helpful for learning or if they cause cognitive load issues

### Intrinsic Cognitive Load

Intrinsic cognitive load refers to mental processing that is essential to completing a task. Intrinsic cognitive load according to Sweller is something that cannot be changed by instructional design but needs to be taken into account by instructional designers Any material that is being learned places intrinsic cognitive load on working memory, the level of difficulty is what changes how much pressure is put on the working memory If a student’s level of expertise is high in the topic being learned then intrinsic cognitive load will still affect working memory just not as much as if the student had little to no knowledge on the topic in question In this case the level of previous knowledge and understanding of the topic in question needs to be taken into account when presenting a class with new information. For example if a person already had some knowledge about oranges, a lesson on the parts of oranges would cause less intrinsic cognitive load than if they didn't know anything about oranges.

### Extraneous Cognitive Load

Extraneous cognitive load is mental processing that does not promote learning and which can be eliminated by changing the design of a task. Extraneous cognitive load is entirely determined by instructional design For example in a multimedia presentation extraneous cognitive load is the sounds, pictures, text, and animations that could be used to present the material. The more that the working memory has to attend to the less likely it is going to retain the information presented Extraneous cognitive load is manageable, good instructional design lessens the load while poor design can increase the load. For example, a teacher is doing a lesson on the life cycle of a butterfly and decides to use a slide show on the smart board. In the slide show the teacher outlines all the relevant information about each part of the cycle but they add an animation of the butterfly evolving through the stages. In this case the extraneous cognitive load would increase because the students have to pay attention to the relevant information while being distracted by the animation.

### Germane Cognitive Load

Germane cognitive load the amount of working memory devoted to processing the amount of intrinsic cognitive load associated with the information presented and is associated only with a learner’s characteristics He notes that germane cognitive load does not cause an independent strain on working memory rather, it is directly associated with intrinsic and extraneous cognitive load levels. For example if we assume that a student’s level of motivation stays constant they have no control over their level of germane cognitive load So what does this have to do with instruction? According to Sweller, this means that if lessons are created to allow working memory to focus on intrinsic cognitive load, by reducing extraneous cognitive load, germane cognitive load is increased and the level of learning increases as well.

### Research and Implications

Intrinsic and extraneous cognitive load are relational, in other words if both are high then working memory can become overloaded The implications are that because only extraneous cognitive load can be controlled instructional designers need to work to keep it low so as not to overload working memory when the intrinsic cognitive load is high According to the theory, in order to reduce extraneous cognitive load we should take advantage of long term memory’s vast capacity by drawing on existing schemas and creating new ones, thereby reducing the strain on working memory These include: presenting goal free problems, useful redundancy, modality, completion problem effect, split attention effect and others
To start, goal free problems were designed by to change student activities to reduce extraneous cognitive load and to encourage schema production They do this by reducing the chance a student will use goal related strategies to try to solve the problem. This is done by changing how a problem is worded so that students don’t limit themselves to trial and error testing which, can take up a lot of working memory’s capacity For example, a math problem asks: a train is traveling at fifty kilometers per hour and travels a distance of 400 kilometers. How long did it take? If a student doesn’t know the correct formula for calculating time when having the above information they will start a trial and error approach to finding the answer, which will increase extraneous cognitive load. However, if the question asked the student to show as many ways as you can to calculate the answer instead it will reduce the extraneous cognitive load on working memory.
The worked example effect is when a person studies already worked examples to learn how to solve a problem, this also reduces the trial and error approach to problem solving because it provides the student a way to create a schema on how to solve these particular types of problems Unlike regular problems, worked problems focus a person’s attention on the steps needed to solve a problem rather than on the problem as a whole, theoretically reducing extraneous cognitive load because nothing else needs to be attended to In this case if a teacher gives students a new equation in math and then proceeds to provide them with a list some of examples where this equation can be used to solve problems the students have a resource to use when it comes to using the equation, which reduces cognitive load.
The theory behind useful redundancy is that if a student is presented with the same information but in different ways they will be more likely to remember it The idea is that because it is the same information just presented in different ways the extraneous cognitive load will lessen because learners choose which way they prefer to attend to the information However, research has since been conducted that brings this claim into question studies have shown that rather than promote deeper learning it lessens it
In a study conducted by Mayer, Heiser, and Lonn a series of experiments were conducted to investigate the redundancy effect in multimedia learning. They define the redundancy effect as a multimedia learning situation where words are presented as text and speech and the learning is hindered by the dual presentation of information In the first experiment 78 college students were tested on retention and transfer of information based on a multimedia presentation on the formation of lightening. The students were divided into four test groups. The no-text/no-seductive-details group received animation and concurrent narration, the text/no-seductive-details group received the presentation with added on screen text that summarized the narration. The no-text/seductive-details group received a presentation that contained text that had irrelevant but entertaining information. The last group received both an on screen text summary and entertaining irrelevant information The results of this first experiment found that the students who received the on screen text summary remembered less on the retention test than those who did not have the on screen text. As well, students who received the seductive details also retained less than those who did not receive seductive details This first experiment falls in line with the theory that over use of details on a multimedia presentation is detrimental to retention of information. They hypothesised that the redundancy effect caused by the on screen text could have been due to the increased cognitive load in the visual channel, or in the auditory channel. The second experiment set out to test this hypothesis by breaking the participants into three groups. The first group contained 36 students who received no added text to the presentation, the second contained 37 students who received a summary of the narration, and the third group contained 36 students who received a presentation with added word for word text of the narration The results showed that the students who received no added text to their presentation remembered more than those who had the added text. They also found that there was no significant difference in retention between the two groups that had added text. The third experiment set out to discover what happens when video clips are added to multimedia presentations. In this experiment the video clips that were added contained information about lightening but that they were not relevant to the specific information presented in the original presentation Thirty eight college students were divided into two groups, the no video clip group and the group that had video clips added to the presentation. They found that the students in the added video group did not remember any more than the no video group but the results failed to reach statistical significance The last experiment conducted looked at whether adding video clips before or after a multimedia presentation boosts interest in the presentation. The results showed that adding video clips to the beginning of a presentation results in students remembering more of the presentation although the results were not statistically significant Overall, this study concluded that adding extra modes of presenting the same information reduced the amount of information that students will retain after seeing a multimedia presentation. When a learner has to divide their working memory to make sense of the information presented the extraneous cognitive load is increased which reduces the amount of information that can be learned. This is especially important when text is added to a presentation. Mayer, Heiser, and Lonn recommend that instructional designers should refrain from adding text when the information is presented orally in multimedia presentations.
Some theorize that those who work on instructional design can go further than only considering ways to reduce extraneous cognitive load. They feel that instructional design can be improved by creating ways to increase germane cognitive load in learners By increasing a learner’s germane cognitive load they feel that the learner’s attention can be directed to the construction of schemas which in turn reduce the strain on working memory during the learning process.

### Summary

In summary Sweller proposes that there are three types of cognitive load and all effect how our working memory is utilized when learning new information. The implications of cognitive load theory on the use of technology in instructional design is that technology can be an effective learning tool as long as guidelines are followed in order to reduce extraneous cognitive load on working memory. In particular teachers need to pay attention to the research conducted on redundancy effect so that they do not overload working memory with redundant information. One way in which technology can be utilized is to present information in ways that help with schema production, which reduces cognitive load by moving information into long term memory.

## Four-Component Instructional Design

Four Component Instructional Design (4C/ID) is an instructional design model developed by van Merriënboer and his colleagues. It prescribes instruction for learning in a complex environment. The 4C/ID model is based on the idea that skills are learned most effectively by using them instead of just reading instructions from a text. It is important that the conditions of learning are similar to what the learner would encounter in real-world applications of the skill, and instruction emphasizes practice rather than information giving. The 4C/ID model consists four components: (1) Learning Task, (2) Supportive Information, (3) Just-in-Time (JIT) Information, and (4) Part-Task Practice (van Merriënboer, 1997;; van Merriënboer & Kirschner, 2007). These tasks are ordered from task difficulty, less complex to more complex. At the beginning of each four components, lots of scaffolding is required, and gradually reduce in amount of scaffolding as learners progress. In this section we will discuss researches and theories about how technology can support this theory of learning.

### (1) Learning Task

Learning Task is represented as circles in Figure 1. Complex learning involves achieving integrated sets of learning goals. The 4C/ID model promotes use of learning tasks that are whole, authentic, and concrete. Learners participating in the online courses, other wise known as technology-based instruction, based on this model it is important to begin learning as a cluster of relatively simple , but meaningful tasks called task classes. It is impossible to provide highly complex learning tasks from the beginning of the training program because this will slow down excessive cognitive overload for the learner. This will lead to learning and performance impairments. Once learners master the simple but necessary components, they progress towards more complex tasks. Complexity of a task is determined by the number of skills involved in task classes, how they relate each other, and amount of knowledge needed to perform them. While there is no increasing difficulty for the learning tasks within one task classes, they do differ with regard to the amount of support provided to learners. This support that a child receives is called scaffolding. Scaffolding is used when needed in situations such as learners moving from the lowest level task classes to the top-level task classes. Dotted lines around the circles in Figure 1 represents the process of selection and development of suitable learning tasks for a child. Eventually, supports and scaffolding fades as a result. Fading support is due to the expertise reversal effect. It is the phenomenon where supports (e.g. coaching) and instructional methods (systematic steps) that works well for novices can have negative effects for advanced learners due to redundancies. It also increases their cognitive load. Learning tasks stimulate learners to construct cognitive schemata by mindfully abstracting away from the concrete experiences that the learning tasks provide. In learning, generalization and discrimination consists schemata to make them more in line with new experiences. According to van Merriënboer, Clark, and Croock these to-be-constructed schemata comes in two forms. Mental Models: that allows reasoning in the domain because they reflect the way in which the learning domain is organized. Cognitive Strategies: guides problem solving in the domain because they reflect the way problems may be effectively approached. Product-oriented and Process-oriented supports are the two ways to applying learning tasks in a classroom setting. Product-oriented support can be divided into highest or lesser degree. Highest product-oriented support is a learning task that provides a case study or worked-out examples that confronts the learner with a given state , a desired goal state, and a solution, intermediate solutions, or both. It is desirable to use accidents, success stories, or stories with unexpected ending to motivate student learning. In these learning tasks, learners are required to answer questions that stimulates deeper processing and the indiction of mental models from the given example materials. By demonstrating a real-life example, learners can get a clear impression of how a particular domain is organized. It is necessary to allow students to come up with their own conclusion/solution. More information can be retrieved from Figure 2. Process-oriented support is also directed towards the problem-solving process itself. A modeling example confronts the learner with an expert who is performing the task while explaining why the task is performed as it is performed. This is a hands-on experience allows children to retrieve information a lot easier than information gathered by reading texts. This method also helps retain information easier than other learning methods. By studying by using the modeling example, learners can get a clear understanding of the systematic approaches and rules of thumb that even professionals use. Thinking aloud may be helpful to bring the hidden mental problem-solving processes as well. Moreover, computer-based learning tools may invite learners to approach the problem at hand as an expert would do.

### (2) Supportive Information

This type of information plays a role in developing complex skill using technology. Learners need information in order to work successfully on nonrecurrent skills (schemata-like controlled processes) aspects of learning tasks and to genuinely learn from those tasks. Procedure-like automatic processes are called recurrent skills in the 4C/ID framework. Complex cognition consists of both nonrecurrent and recurrent skills. Supportive information is provided to help learners master the nonrecurrent aspects of complex cognitive task. It provides a bridge between learners' prior knowledge and the learning tasks. It is the information that teachers typically refer it to the theory and often presented during lectures or in study books . The goal of supportive information is to help learners acquire the different kinds of flexible schemata needed to cope with real life problems. Supportive information plays as an additional to or an elaboration of the previous information and help students to establish factual relationships between newly presented information elements and their prior knowledge. It allows learners to do things that could not be done before. It has been shown that this type of elaboration process produces highly complex schemata that should allow for deeper understanding. Learners may study how databases are organized in order to develop useful mental models. Task performers further develop their mental models and cognitive strategies in order to improve their performance. For example, Tiger Woods makes extensive study of the layout of golf courses to develop mental models of how they are organized. Also by him watching videotapes of his competitors help him develop cognitive strategies of how to approach problems in this world (real-world). It is of utmost importance to stress non-arbitrary relationships. Methods that identify relevant relationships can be used in an expository fashion or in an inquiry fashion. Expository methods allows learners to explicitly present the non-arbitrary relationships. Inquiry methods ask the learners to discover the relationships. Within these two methods, experiential one is the most important relationship. It relates general and abstract knowledge to concrete cases. The 4C/ID model furthermore distinguishes inductive and deductive strategies for presenting supportive information. There are two types of inductive strategies. Inductive-Inquiry Strategy is a method that presents one or more case studies and then asks the learners to identify the relationships between pieces of information illustrated in the case(s). However, this method is very time consuming and requires deep level of understanding although learners have no experience with the skill. Therefore van Merriënboer, Clark, and Croock (2002) does not recommend using this method unless there is enough instructional time available. Inductive-Expository Strategy on the other hand, starts with one or more case studies and then explicitly presents the relationships between pieces of information that were illustrated in the cases. Merriënboer, Clark, and Croock (2002) suggests using this approach by default since this strategy is more reasonable and time effective by starting with concrete, and recognizable case studies that works well for learners with little prior knowledge. Cognitive Feedback is known as a final part of supportive information. This refers to the nonrecurrent aspects of performance since nonrecurrent performances are never correct or incorrect, it is rather more or less effective. Cognitive feedback can only be presented once learners have finished one or more, or all, learning tasks. When feedbacks are well-designed, it should stimulate learners to reflect on the quality of their personal problem-solving processes and founded solutions.

### (3) Just-in-Time (JIT) Information

In contrast to supportive information, JIT information is aimed at the recurrent aspects of complex skills. It is the prerequisite to the learning and performance of recurrent aspects of learning tasks or practice items. Automaticity depends heavily on consistency, and repetitive practice. JIT information gives learners the step-by-step guidance when needed then fades quickly. The goal of JIT information is to make basic, but critical skills as automatic as possible, as soon as possible. Freeing cognitive resources, leading to more automaticity becomes crucial to advanced learners. It also provides a step-by-step knowledge, such as teachers or tutors directing learners almost acting as an assistant looking over their shoulder. JIT information is identical for many learning tasks, therefore it is typically provided during the first learning task for which the skill is relevant. Similarly to scaffolding, JIT information goes through a principle called fading, that is a quick fade as learners gain more expertise in the learning material. Instructional method of JIT information mainly promote complication through restricted encoding of situation-specific knowledge into cognitive rules. These rules are formed through multiple practice and this process is when information is necessary for forming the rules is directly available from our working memory. Applying this into a real-life situation, for instance, when one is learning golf, your coach will preferably explain how to hold a club, taking stances, and making swings out on the driving range while making first drives, and not during a lecture in a classroom. This goes the same for learners in a classroom setting. Information Displays is organized in small units, this is considered to be essential because controlling the number of new information to bear minimum prevent processing overload during practice. In a real-life situation, for instance, a manual for complex machine may explain the steps one by one rather than assuming user's prior knowledge and only stating some of the steps. This approach should directly present information displays when the learners need the information to work on the recurrent aspects of a particular learning task. However, in some situations this approach is not always helpful. Training for a job, for instance, learning aids such as on-line help system, checklists, and manuals are available and readily accessible. This is due to lack of direct presentation of JIT information when necessary. Demonstrations and Instances are the name for elements of the recurrent skill, also known as generalities. Just like rules can be applied in various situations, these are called demonstrations; for concepts, plans, and principle, on the other hand are called instances. Cognitive Feedback is considered as a final part of JIT information which relates to feedback that is provided on the recurrent aspects of performance. This feedback should promote compilation, meaning that if rules are not correctly applied to the situation, learners are said to make an "error". These feedbacks are recommended to be presented as early as possible. This is for learners to correctly input the right information into their working memory. The 4C/ID model genuinely believe that errors are inevitable in learning and it also plays an important role in a sense that learners learn to recognize their own mistakes and errors, and learn how to recover from them.Well-designed feedbacks should inform the learner why there was an error and provide suggestions or hints of how to achieve their goal. It becomes crucial not to give out answers to encourage their learning process.

### (4) Part-Task Practice

Learning tasks are designed to promote schema construction, and also facilitate compilation for recurrent aspects of the complex skills. The last component of 4C/ID model, part-task practice provides additional practice for selected recurrent skills in order to reach required level of automaticity. It is a way of automatizing procedural knowledge more rapidly while circumventing cognitive load problems resulting when learners try to develop skills while simultaneously trying to solve a problem. Expertise is ordinarily a slow-developing process that depends on extending practice to automatize the productions that directly control behavior. JIT information presentation aims at restricted encoding of newly presented information in rules. Learners practice would be supported through appropriate JIT information until they achieve automaticity. van Merriënboer and his associates believed that some part-task practice can help reduce task complexity due to relatively short and spaced periods of it intermixed with work on complex, authentic tasks. This pattern allows the learner to practice sub skills and relate them to the overall task. It is important that practiced items are divergent for all situation/environment that underlying rules can deal with. However, when a high level of automaticity of recurrent aspects are required, learning tasks may provide insufficient repetition to provide necessary amount of strengthening. This is when we need to include additional part-task practice. Other situations such as learning in a general environment, part-task practice is not helpful to complex learning. Part-task practice promotes the compilation of procedures or rules and specially their subsequent strengthening. These are a very slow process that requires numerous practice items. Examples for part-task practice are multiplication tables or playing scales on musical instruments. It becomes critical to start part-task practice within an appropriate cognitive context since it has been found effectively only after learners were exposed to an easier version of the complex skill. Task hierarchy indicates that either, they enable the performance of many other skills higher in the hierarchy, or it has to be performed simultaneously with many other coordinate skills. Therefore, one should identify the first task class then initiate part-task practice.Practice Items for par-task practice encourages learners to practice number of times just like the saying, "Practice makes perfect". However, learners have to keep in mind that the whole set of practice items should be divergent, and be applicable in all situations. This will help develop a broad set of situation-specific rules. In cases such as highly complex algorithms, it may be necessary to work from simple to complex practice items to decompose it into parts then gradually combine towards the whole task. This approach is called a Part-Whole Approach. Right use of part-task practice will lead to accurate performance of a recurrent skill. Furthermore, extensive amount of overtraining may be necessary to make the skill fully automatic. For tasks that highly relies of automaticity, sometimes the ultimate goal is not be accurate. It is common, in such cases, that acceptable accuracy combined with high speed and performance skills as a whole is the goal. In order to reach this, the recurrent skills are first practiced under speed stress, then once the speed criteria is reached, the skill is practiced under time-sharing condition. Only then, the skll is practiced in the context as a whole task. In other words, performance criteria gradually change from accuracy, to accuracy combined with speed, to accuracy combined with speed under time-sharing conditions or high overall workload. It is suggested that short, spaced periods of part-task practice or overtraining has better results than long, concentrated periods of part-task practice. Part-task practice is best intertwined with the learning tasks because this provides distrubuted practice and also enables the learners to relate the recurrent constituent skill to the whole complex skill.

### Research and Implementation

Recent study on the effectiveness of learning environments using one-by-one-by-two pretest-posttest quasi-experimental design from Frederick K. Sarfo and Jan Elen (2007) concluded that 4C/ID method combined with Information and Communication Technology (ICT) showed the best result in learning gains. The dependent variable was the learning gain which was calculated by subtracting pretest score from posttest scores. The independent variable was the tree treatment conditions. Three groups compared were; regular method of teaching vs 4C/ID learning environment with ICT vs 4C/ID learning environment without ICT. The sample consisted of 129 students selected from six Secondary Technical School in Ghana with the age mean of 18 and Standard Deviation of 1.3 years. Assessment tasks consisted of 26 pretest and posttest items; 13 retention and 13 transfer test. Result revealed a statistically significant difference between student's pretest and posttest in all three groups. With average pretest across all groups being 6.28, the average posttest across all groups were 14.39. Taking a closer look into the data presented by Frederick K. Sarfo and Jan Elen (2007), study claim that 4C/ID learning environment with ICT scored higher in both pretest and posttest. Researchers conclude that these results indicates that the experimental group was better able to solve problems that required reasoning, reflection and recall of procedures, facts and concepts.
Using this Four Component Instructional Design in a classroom setting will help students learn better specially in a complex environment. In order to apply this model, teachers who are teaching the material should be an expert in the field. This will help answer all questions that students may have and helps children understand the course material deeper. Additional support from media or technology specialist may be required. Most importantly, in this model, it becomes essential for teachers-students, and student-student to work as a collaborative team.

### Summary

Four Component Instructional Design model is based on research on cognitive learning and expertise. It provides a framework for designing technology systems for developing complex skills. According to the model, experiences should be realistic and increasingly more authentic tasks; such as projects, cases, and scenarios. Instructions given to the learner should focus on practice and not information giving. These components will be practiced until one achieves the required level of automaticity, without any scaffolding. Once children accomplished all four components, it can be said the one mastered the knowledge or activities. Most importantly, the 4C/ID model does not propagate the idea of errorless learning. The 4C/ID model should be used to develop training programs for complex skills and when transfer is the overarching learning outcome. This model is not developed for teaching conceptual knowledge or procedural skills, and not useful for designing very short programs. Despite all these studies, further research continues on Four Component Instructional Design model.

## Collaborative Learning

Learning collaboratively through pieces of technology systems
As technology is becoming more advanced so are its uses in which individuals can gain and share information. Collaborative learning which is sharing and learning knowledge through peers/groups has become a focal point for different interactions through technology systems. Social interactions are an important factor in cognitive growth. Student interactions with their peers and teacher are among the most important of these exchanges. However, a question that comes up is how can technology help or incorporate these types of interactions. Ways in which good technology design can help students is to note how our cognitive system works, these are things such as attention, working memory, and long term-memory as well as how complex cognitive skills develop. An example of this is the need for supportive and JIT (just-in-time) information, coaching, and scaffolding for effective learning strategies. A key thing to remember is that a good design system works with our cognitive systems. This section will be broken down into different models of technology designs and how collaborative learning may or may not be effective within these systems, The different models in which it will be broken down are learning through/from experts, learning with peers, learning through inquiry, learning through creation, and learning through games. Collaborative learning is seen as a great tool for teachers and students when it comes to education and information being taught or shared. It allows students to experience what it is like working with other peers. However, although it can be seen as a great system to have and incorporate in classrooms it does have some flaws and are still being fully developed to be used in the most effective ways for teachers and students. Teachers should not heavily rely on these types of technology systems but they can be useful and informative.
One of the first things to consider when discussing various systems of technology and their possible implications is how do people learn? It is often seen that many students have trouble with learning information because they are more focused on memorizing rather than understanding. However, Nobel Laureate Herbert Simon said a great piece that “the meaning of ‘knowing’ has shifted from being able to remember and repeat information to being able to find and use it.” In order for students to develop a better understanding in subject matter they must have a deep foundation for factual knowledge, understanding facts and ideas in the context of a conceptual framework and organize knowledge in ways that facilitate retrieval and application. What does this mean exactly in terms of knowledge? Having a deep foundation of factual knowledge, students are aware of the information that is true and relevant to what they are learning. Understanding facts and ideas in the context of a conceptual framework, meaning students understand the material in the context that it is placed in, and how it relates to that topic. Organize knowledge in ways that facilitate retrieval and application, is helping students take that knowledge that they have or are learning and being able to apply it to other areas or topics. These requirements do however have some difficulties when it comes to implementing them in class or within the curriculum. It becomes difficult for teachers because students come into the classroom with these preconceived notions about what they already know. As well as, teachers have a set amount of information they need to teach, it becomes difficult when they have to go into depth in every topic or re-teach certain areas multiple times. As students progress through their school careers many teachers believe they are taught certain material from the previous years, this is not always the case. Some students may feel like they are behind, or are too afraid to ask questions and ask for help. This is where the incorporation of technology systems may be able to help or at least ease the pressure off teachers and allow students to use them within the classroom or on their on time. Now these technology systems are not to take the place of the teacher but rather complement the teacher's lesson. It is not their job to be the foundation for learning but instead they can act as a review to help students with exams or projects. Teachers who rely too heavily on these technology systems may lose a lot of material and interaction that the students can only receive from a physical being. Believing that technology can take the place of teachers is not the proper way of looking at the systems that are being created, they should be viewed as more of a tool to help aid those who take advantage of using them.
To give a brief descriptions about what these systems are-
Learning from Experts: Cognitive Tutors & Telementoring
The first type of technology system is learning from experts, two examples of this are known as cognitive tutors and telementoring. Cognitive tutors, is “a type of intelligent tutor that supports ‘guided learning by doing’”. Cognitive tutors are based around John Anderson’s ACT theory. This theory contains three main principles, the first one is procedural-declarative distinction, the second one is knowledge compilation, and the third one is strengthening through practice. The main focus of cognitive tutors is to monitor students learning as well as provide them with context specific feedback when a student needs it. The primary focus for cognitive tutors are in the areas of mathematics and computer programming. This is able to help students get a better understanding of material while working at their own pace they can also work with others to solve and work through the problems together. One study that was done with cognitive tutors, was a study done by Kenneth R. Koedinger, called Intelligent Tutoring Goes To School in the Big City. In this study “The Pittsburgh Urban Mathematics Project (PUMP) [had] produced an algebra curriculum that is centrally focused on mathematical analysis of real world situations and the use of computational tools. We have built an intelligent tutor, called PAT, that supports this curriculum and has been made a regular part of 9th grade Algebra in 3 Pittsburgh schools. PAT was useful because it was able to help students who had difficulty learning in classrooms. In the 1994-95 school year, the PAT curriculum expanded to include 10 lessons and 214 problem situations. Students are in the computer lab two days a week, working with PAT at a self-paced rate. Student time on the tutor will more than double (roughly from 25 to 70 days) compared to the 93-94 school year.” Telementoring or better known as ‘e-mentoring’ or ‘online-mentoring’ provides students with the opportunity to work with another individual with problems they may be having with course material. Mentoring interactions occur with problems that students are having and questions that they think of. A downfall to telementoring is that students do not get to work with the same adult over and over again. Although they are collaborating they do not get to build a connection with the mentor as some students do with teachers. They do not get the physical one on one interaction with a teacher and the connection is different in comparison to virtually speaking/learning from an individual. This can also be a pitfall with cognitive tutors and learning through any software is not building a relationship with a teacher or mentor and feeling as if there is a disconnect.
Learning with Peers: Knowledge Forum & Starburst
Knowledge forum is a collaboration platform for students to build upon ideas. It places emphasis on community rather than the individual. Knowledge forum is a place where students or individuals can create databases where knowledge is built, this is where collaboration is highly involved. The main components of knowledge forums are what are known as notes and views. A view is a way to organize the notes made by individuals, this can take the shape of a concept map, a diagram, or anything that visually adds structure. the notes appear within these structures. This is also great because it involves the concept of visually learning as well because through the diagrams and maps students are able to connect ideas and see how the connections are made. This is a way for students to all work together on a topic and provide information on a database that can continuously grow. However, knowledge forums are not the only place students and individuals should get their knowledge experience. Learning material through books, and lectures, as well as going on field-trips allows individuals to get a better understanding and perspective. Knowledge forum is just a database where the topic is shaped and evolves. Similar to knowledge forum starburst also provides a place for students to collaborate with others in sharing ideas through a database. However starburst the ideas spread out like a web getting larger and larger. These two systems mainly focus on peer interactions and collaboration among individuals in order for knowledge to build and grow. A study that was shown using knowledge forums was done by Carol and Yuen Yan Chan. In their study, which is taken directly from their article written: “The sample includes 521 secondary school students in Forms One to Six (ages 12–17) from eight secondary schools in Hong Kong. These participants were involved in a research project on computer-supported knowledge building. The sample includes 322 male and 199 female students, with 216 from junior high (Grades 7–9, ages 12–14) and 305 from senior high schools (Grades 10–12, ages 15–17). Students in Hong Kong are streamed into different bands according to their academic achievements; there were 267 students from high-band schools and 254 students from low-band schools.This study took place in the context of a University-School Partnership project on developing knowledge-building pedagogy for elementary and secondary teachers in Hong Kong. The context of the project included university researchers/mentors providing professional development to teachers. There were regular workshops throughout the year to help teachers better understand knowledge-building epistemology and pedagogy; groups of project teachers meeting to plan their curricula collectively; and classroom visits with university researchers and teachers. Regarding knowledge building pedagogy, in a typical knowledge-building classroom, students usually start by identifying areas of inquiry and putting forth their ideas and questions, ‘making ideas public’ for collective improvement is emphasized. In Asian classrooms, it is particularly important for students to experience working together as a community. In this project, classroom and online discourse were integrated, with students contributing notes to Knowledge Forum as they engaged in collaborative inquiry – posing questions, putting forth ideas and theories, building on others’ ideas, and co-constructing explanations to advance their collective knowledge. Data were collected from two questionnaires examining students’ views of collaboration and online learning, and their preferred approaches to learning. After examining the questionnaire data, we excluded items on online learning that showed variable responses, and focused on the questionnaire items on knowledge-building and approaches to learning. We also employed students’ usage statistics on Knowledge Forum derived from Analytic Toolkit to examine their online forum participation.The questionnaire, comprising 12 items, written in Chinese, examined students’ views of collaboration aligned with the notion of knowledge building. Students were asked to use a 5-point Likert scale to rate the questionnaire items that reflected their experience of collaboration while working on knowledge building. In assessing these items, the students could refer to both face-to-face and online collaboration To measure students’ online forum participation, Analytic Toolkit was used to retrieve and analyze summary statistics on individual students’ activity in Knowledge Forum. Analytic Toolkit Version 4.6 provides up to 27 analyses to show how students interact with each other in the Knowledge Forum database. We selected several of the most frequently employed indices from previous studies, including those that have been grouped into overall indices with good construct validity with quality of forum writing (e.g., van Aalst & Chan, 2007; Lee et al., 2006; Niu & van Aalst, 2009). The indices are as follows: (i) Number of notes written: This is included because it is the most commonly used index for measuring online participation. (ii) Scaffolds: This index refers to the number of scaffolds (thinking prompts) used. Knowledge Forum includes scaffolds such as “I need to understand”, “a better theory”, and “putting our knowledge together”. Scaffolds help students to frame ideas and to signpost their ideas to others for interaction and dialogue. (iii) Revision: Students’ attempts to revise their notes are recorded. From a knowledge-building perspective, revision shows a deeper approach to working with ideas. Instead of employing a linear approach, ideas are revisited and revised based on the contributions of the community. (iv) Number of notes read: The number of notes read has been considered important for assessing community awareness; one cannot engage in dialogue without knowing what others have written (Zhang et al., 2009). (v) Number of build-on notes: This index is different from the number of posted notes, and refers to responses to previous notes. This index provides more information about interaction among participants. (vi) Keywords: Students can include “keywords” when they write notes on Knowledge Forum. Other participants can use these keywords to search for related notes on similar topics. The use of keywords reflects domain knowledge and community awareness as students try to make their work more accessible to other members.”
Learning through Inquiry: Anchored Instruction & WISE
The best example of anchored instruction is known as The Adventures of Jasper Woodbury Series. These series are complex video-based problems and it was created so that each of the Jasper adventures are focused on a complex math-oriented problem that needs to be solved. Because such math problems are very complex they are often too difficult to solve alone. While working together, students are able to come up with more than one right solution, and are needed to provide evidence as to why they think theirs is correct. This involves collaboration among the students to come up with various solutions to the problems given because there is not only one right answer. Another way students can work together to solve problems is through what is called WISE (Web-based Inquiry Science Environment). Students work together in a web-based environment and discuss problems to do with global warming or recycling. With WISE teachers are able to play a supportive role and monitor what the students are providing as solutions. “WISE provides evidence and hints about the topic; notes, visualization, discussion, and assessment tools; and prompts for collaboration, reflection, and design of solutions” The big ideas with anchored instruction are that students are learning by constructing understanding as well as learning in context. There is generative learning that occurs as well and this is where sub-goals are created. The big ideas with a program such as wise are that learning is intentional and students are integrating prior knowledge when answering questions.
Learning through Creation: Scratch
An example of learning through creation is the program Scratch. It is a website that media and visuals is the main component. In scratch students are able to work individually or as a group to make visuals in a program for an online community. They are able to share these visuals with one another. With Scratch the students are in control and are able to think with the use of objects, as well as create something from their own imagination. They can encompass audio alongside their visual creations. Students work together in creating these pieces and can share them in the classroom as part of a project, or teachers can base it off a theme or topic they are learning. This helps students think creatively and work collaboratively. An example of how Scratch can be used by teachers in their lesson plan is imagine you are in your 8th grade history class and for your final project you have to choose a topic that you have learned about within the semester and create a visual representation of it. Whether the project be completed in groups or done individually. You work with other members and decide to use the program Scratch, you begin to create different characters such as wounded men, and soldiers etc. You and your group members discuss ideas and begin to create each idea piece by piece. Slowly the image your group had in their mind is creatively coming to life. You are now able to see the piece of history you learned in a visual picture and you can share it with your other classmates.
Learning through Games: Quest Atlantis
Another way students can work collaboratively is through games, one game in particular is Quest Atlantis. This provides different scenarios and realms for students to venture through as they come across problems and tasks they have to choose from and solve. It is an engaging game however it may not fit in classrooms but rather in the spare time of students. The context is best for providing situated learning; this is learning that takes place from social relationships and connecting prior knowledge to new contexts.
Collaborative learning through the use of computer programs is another great way to get students engaged with materials. It does have setbacks in the ways in which they can be used and incorporated into classrooms. Teachers may not have enough devices for students to use as well as students can become unfocused and begin to just play around with the programs. For the programs that provide hints when there is a problem occurring, students can just continuously be getting hints without even trying. Although there a positive to these technology systems, one must take into considerations the negative implications as well. As mentioned none of these systems should be the primary bases of students learning, instead they should be a supplementary add on for students and teachers to use.

## Glossary

Cognitive Load Theory: a theory proposed by John Sweller and focusses on working memory and instruction.
Cognitive tutors: A type of intelligent tutor that supports ‘guided learning by doing’
Collaborative learning: sharing and learning knowledge through peers/groups
Expertise reversal effect: phase where supports and instructional methods have negative effects on individuals due to increase in cognitive load
Extraneous Cognitive Load is the way working memory is affected by the material is presented
Germane Cognitive Load: the amount of working memory devoted to processing the amount of intrinsic cognitive load associated with the information presented and is associated only with a learner’s characteristics.
Intrinsic Cognitive Load: refers to the way in which information is presented.
Nonrecurrent skills: tasks that are effortful, error-prone, easily overloaded, and require focused attention; =schemata
Recurrent skills: correspond to procedures; they occur with little or no effort, are data-driven, and require little or no conscious attention
Situated learning: learning that takes place from social relationships and connecting prior knowledge to new contexts.
Task classes: principle of working from a simple to complex or meaningful task

## Suggested Readings

* Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2

## References

* Anderson, J., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned: Journal of the Learning Sciences, 4(2), 167-207.
* Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* Barab, S. A., Dodge, T., & Ingram-Goble, A. (2008). Reflexive play spaces: A 21st century pedagogy. Games, Learning, and Society, Cambridge University Press, Cambridge, MA.
* Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* Bollen, L., Harrer, A., Mclaren, B. M., Seawall, J., & Walker, E. (1995) Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Direction
* Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* Craig, S., Gholson, B., & Driscoll, D. (2002). Animated pedagogical agents in multimedia educational environments: Effects of agent properties, picture features and redundancy. Journal of Educational Psychology, 94(2), 428-434. doi:10.1037//0022-0663.94.2.428
* Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* Kevin O'neil, D., & Harris, J. B. (2004). Bridging the perspectives and developmental needs of all participants in curriculum-based telementoring programs. Journal of Research on Technology in Education, 37(2), 111-128
* Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In R. K. Sawyer (Ed).The Cambridge handbook of the learning sciences (pp97-118). New York: Cambridge University Press.
* Salisbury, D.F., Richards, B.F., & Klein, D. (1985). Designing practice: A review of prescriptions and recommendations from instructional design theories. Journal of InstructionalDevelopment, 8(4), 9- 19.
* Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. ETR&D Educational Technology Research and Development, 53(3), 47-58.
* Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5.
* Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* van Merrienboer, J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development 53(3), 5-13.

## Citations

* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5
* ↑ Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ van Merrienboer, J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development 53(3), 5-13
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Salisbury, D.F., Richards, B.F., & Klein, D. (1985). Designing practice: A review of prescriptions and recommendations from instructional design theories. Journal of InstructionalDevelopment, 8(4), 9- 19.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* ↑ Bollen, L., Harrer, A., Mclaren, B. M., Seawall, J., & Walker, E. (1995) Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Direction
* ↑ Anderson, J., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned: Journal of the Learning Sciences, 4(2), 167-207.
* ↑ Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997).Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* ↑ Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.