A Unified Instructional Strategy Jayasurya Venugopalan, Wipro Ltd, Karnataka, India Annapoorna Gopal, Wipro Ltd., Karnataka, India Abstract: Direct learning methods have been the traditional forms of learning from time immemorial. The late 60’s saw the rise of inquiry based and experiential learning models which have attained some degree of success. More recently neuroscience based learning models have become prevalent. The paper attempts to arrive at a new model for instructional approach which integrates direct learning, experiential learning and cognitive neuro-scientific learning principles to arrive at the Unified Instructional Strategy. Due importance is given to the architecture of human memory. The semantic declarative and procedural aspects of long term memory are considered so as to more efficiently associate new learning to existing learning, thus cementing this into long term memory. The methodology involves a 5 stage approach: (i) Review of learning already present in participant memory. (ii) Introduction of new concepts linking them to known concepts (iii) Introduction of new principles and procedures and relating them to the concepts learnt. (iv) Give a wider perspective to the concepts and principles /procedures learnt (v) Expose the participants to a problem solving situation which applies the new learning. This 5 fold approach to instructional strategy will help participants grasp the subject better and enable its storage in long term memory. Keywords: Direct Learning, Experiential Learning, Inquiry Based Learning, Neuro-Scientific Learning, Unified Instructional Strategy, Long Term Memory, Concepts, Principles, Procedures, Problem Solving
Introduction
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HE CONCEPTS OF direct, experiential and problem solving methods of learning are well known and have been implemented in diverse environments. Neuroscientific methods too have been investigated over the last few years and have been used to fuse learning with human memory architecture. The approach outlined in this paper integrates and unifies direct, experiential and neuroscientific methods to evolve an instructional strategy framework which is adaptable and effective. It would enable and equip the learner to acquire knowledge and to leverage it to the greatest advantage.
Learning Models and their Evolution The importance of learning models in any form of learning is extremely significant. The most frequently used learning models are: 1. 2. 3. 4. 5.
Direct Learning Model Experiential Learning Model Inquiry Based Learning Model Problem Based Learning Model Neuroscience Based Learning Model
The International Journal of Learning Volume 17, Number 2, 2010, http://www.Learning-Journal.com, ISSN 1447-9494 Š Common Ground, Jayasurya Venugopalan, Annapoorna Gopal, All Rights Reserved, Permissions: cg-support@commongroundpublishing.com
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Direct Learning Model was the first to evolve and it concentrated on direct instruction from faculty to learners . However, the subsequent Experiential and Inquiry-Based models paid more attention to learning by doing and faculty student interaction. (“Inquiry Based Learning”; Kahn & O’Rourke, 2005). The Problem Based Learning Model on the other hand emphasized on learners solving real life problems to acquire knowledge. (“Problem Based Learning”; Venkatesan & Fragomeni, 2008). Of late, the Neuroscience Based Learning Model has introduced learning techniques based on the architecture of human memory and brain (Byrnes, 2001).
Architecture of Memory and Learning Many models of memory have been proposed and Dr Milton J. Dehn has evolved an elegant integrated Model of Memory based on existing memory models, which we shall use in this paper.
Fig 1: Integrated Model of Memory (Dehn, 2008, pp 51) As seen in the diagram the components of this integrated memory model are: 1. 2. 3.
Short-Term Memory: Visuospatial and Phonological Working Memory Long-Term Memory
Short-term memory stores verbal (also known as phonological) and visuospatial information obtained typically through the auditory and visual senses. It can automatically encode this information into long-term memory, bypassing working memory. When needed short-term memory also activates long-term memory structures.
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Working Memory acts on both short-term memory and long-term memory information. Its operations include encoding information into long-term memory, association of memory, transforming information, searching, chunking and creating new memory representations. It basically helps the brain to process information and store what has been processed. Long-Term Memory is composed of: 1. 2.
Semantic Memory and Episodic Memory
Semantic memory can be Declarative or Procedural memory. Declarative memory stores facts, concepts, principles and rules, organized according to classifications, associations and meaning. These are stored as semantic structures which form a network or schema. As learning occurs schemas are changed and connections between related schemas are made stronger. On the other hand, procedural memory is a record of the various steps required to complete a task. For successful learning, semantic memory must be well organized for both declarative and procedural knowledge. Episodic memory is primarily visual and contextual, and is focused on specific events or episodes. Long-term memory can also be classified into explicit and implicit memory. Much of the long-term content in working memory is brought by automatic activation, directly initiated by short-term memory. As knowledge and skills become entrenched in long-term memory, less processing is required (Lewis-Peacock & Postle, 2008) When automated activation and retrieval and processing are insufficient for a task, working memory initiates a search for retrieving as well as for active restructuring and recoding. To achieve this working memory operates on the semantic memory structures in long-term memory. (Dehn, 2008; Anderson, Reder & Lebiere, 1996).
Direct Learning vs Experiential Learning Direct Learning Methods have been traditionally used for learning from the onset of education. However in the late 60s inquiry based and experiential learning models evolved and started becoming popular. Of late, inquiry and experiential based learning were felt inadequate for present learning demands. Another opposing school of thought, however, maintained that motivational levels can be triggered and sustained only through inquiry, experiential and problem based learning. Let us first investigate these two proponent theories before postulating a probable solution. Learning involves both short-term and long-term memories. Our long-term memory has a huge knowledge base that is central to all our cognitive activities. The works of De Groot on chess expertise, Chase and Simon, and Egan & Schwartz1, all suggest that expert problem solvers obtain their skill by using the large amount of experience stored in their long-term memory and then rapidly determining the best procedures for solving problems. When a person has huge amounts of strongly linked information in long-term memory he becomes an expert. This is because the associated information in his long-term memory enables him 1
De Groot was a chess master and psychologist who conducted some famous experiments in chess in the 50s and 60s. Chase and Simon as well as Egan and Schwartz were psychologists who studied behavior of memory in chess games and positions in the 1970s.
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to quickly recognize the characteristics of a situation , even if it is new, and enables him to determine how to resolve the problem encountered.. The objective of any instruction is to meaningfully alter long-term memory. If nothing changes in long-term memory, nothing has been learned. The architecture of long-term memory guides us how to conduct instruction optimally. Working memory has two major characteristics: 1. 2.
It can process only for a limited period of time. It has limited capacity.
Information in working memory if not rehearsed is lost within 30 seconds and its capacity is limited to only a very small number of elements . Information in long-term memory can be activated and accessed by working memory. Moreover while accessing this information in long-term memory, the capacity limitations of working memory are not applicable. If we ask novice learners to find solutions for problems using unguided or minimally guided instruction mechanisms, they will not have sufficient relevant information in longterm memory. So the capacity limitations of working memory will apply and problem solving will become quite difficult. To build any set of skills or expertise the learning must be embedded in long term memory. To do this we need to have instructional learning designs which give quite a considerable amount of direct guidance (Kirschner et al., 2006). This is what the advocates of direct learning state. Let us now turn to the opposing school of thought, which is the proponent of inquiry based experiential and problem solving learning with minimal guidance. Kuhn (2007) says that learning should emphasize more on the subject matter that students learn and the relation between a particular student’s inclinations and the specific content required to be learnt. Motivation does not reside within the individual but is in the interaction between the individual and content. Inquiry based or learning by doing is irreplaceable. Students need to acquire complex skills and competencies nowadays. To learn what professionals do and how they do it, students should engage themselves , in however rudimentary a way, in a hands on approach. This means that supervision or guidance must be minimal for students to learn on their own.
Reconciling the Two Schools How then do we reconcile these two approaches? Are they divergent or can they be unified in a holistic way? Kirschner et al (2006) says that for learning to happen it must be stored in long-term memory. At the same time Kuhn (2007) and others say that motivation is enabled only when students learn on their own and by practice acquire the skills needed of them. The problem then reduces to imparting learning which can be stored in long-term memory. It is apparent that the experiential approach to learning is ideally suited for adult learners as: 1. 2.
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They already have a considerable store of knowledge and experiences These groups are motivated more by the experiential approach
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It must be agreed that learning happens only when the student is left to think and reason out on his own. Instructional design then is of paramount importance. The learning has to be such that the learner acquires sufficient knowledge in long-term memory before embarking on self discovery and learning by doing. These learning processes have to be interspersed judiciously, with guided instruction on one hand building up long-term memory followed by experiential learning which helps to sink in the learning further into long-term memory. This is not enough. We have to scale the learning content in such a way that the learner navigates from the basic to the complex in a natural manner, so that his learning goes into long-term.
Instructional Strategies Before discussing the Unified Instructional Strategy Model, let us first summarize the various instructional strategies which have been employed in learning.
Types of Organizational Instructional Strategy The important types of organizational instructional strategies are (Smith & Ragan, 2005): 1. 2. 3. 4. 5.
Declarative Knowledge Instruction Strategy Concept Learning Instructional Strategy Procedure Learning Instructional Strategy Principle Learning Instructional Strategy Problem Solving Learning Instructional Strategy
Declarative Knowledge Declarative knowledge can be categorized into three different subtypes. 1. 2. 3.
Knowledge Acquisition of Labels and Names: Knowledge Acquisition of Facts and Lists Learning of organized discourse
Knowledge of labels/names involves learners connecting two elements in declarative memory. Though meaning of the elements need not be necessarily known, linking becomes difficult without knowing the meaning. A fact is a statement which normally describes a relationship between two or more concepts. For example: A dog is a household animal This links dogs to the category of household animals. A list is a set of items which has to be remembered in toto. Facts and lists must be integrated with prior knowledge in memory. As the semantic tree becomes larger and more complex it becomes easier to add new declarative knowledge by linking, elaborating and organizing. Learning of organized discourse involves the comprehension of a line of reasoning like in a passage in a book. (Scott, “Teaching Declarative Knowledge�) (Smith , 2006)
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Concept Learning “A concept can be defined as a set of specific objects, events or symbols which are grouped together because of their shared characteristics and which can be referenced by a common group name or symbol “(Merrill and Tennyson.1977). Concepts can be both concrete and abstract. Dog is a concrete concept whereas happiness is abstract . A learner who has acquired a new concept is able to use it to identify instances of that concept he may not have been exposed to earlier. Similarly concept learners can illustrate concepts learnt with their own examples and apply them appropriately. Concept Learning involves generalization and discrimination. Discrimination is to ensure that members of other similar groups are distinguished from the concept under study. For example cows should not be classified under dogs, this requires discrimination. Concepts are stored in semantic long-term memory as productions. For example the concept of a square may be stored thus: IF the figure is a parallelogram And each side is equal And each internal angle is a right angle THEN the figure is a square (Smith, 2005; “Concept Learning”): Procedure Learning Procedures are typically defined without ambiguity, where all steps are included and each step is clear. Procedures can be simple or complex. In a complex procedure there are many decision points, at which the learner must decide which of two or more situations exist and correspondingly choose the next step in the procedure. Instruction by which the learners understand the principles relating the relevant concepts of a task and apply the procedure to accomplish the task would be very effective in forging the association between the procedure in long-term procedural memory and the concepts and principles in declarative memory. Instructional strategies should explain the procedure in the context of the underlying principle so that information is more easily learnt. The learner must understand the concepts before learning the procedure . Procedures also require that productions be learnt. The following is an illustrative format: IF the scenario possesses unique features A,, B or C usually understood as concepts, THEN apply procedure P1 (Smith, 2005; Wilson, 1995). Principle Learning Principles describe the relationship between two or more concepts. These relationships can be represented by if-then or cause-effect relationships. Knowing a principle helps the learner to explain what has happened and also how to control the effects of variabes involved in the principle on each other. Principles are a type of declarative knowledge. Learning a principle also implies the ability to apply the principle in a spectrum of scenarios which the learner has not been exposed to earlier.. The cognitive processes of the application of principles can be represented as productions. Principle learning is central to problem solving. To find and solve problems in
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a domain, learners must have acquired a large number of principles that explain the relationships between critical concepts in that domain. Information analysis for principles is somewhat different from that of procedures Some examples of the processes of human cognition involving principles are shown: 1. IF the situation described involves key concept X And if the situation involves key concept Y THEN rule Q applies in the case. (Concept Recognition) Here Q is a principle. 2. IF concept X changes in direction D1 with magnitude M1 THEN concept Y will change in direction D2 with magnitude M2. This principle explains the relationship of the two concepts. (Smith, 2005; “Principle Learning�). Problem Solving Learning Problem solving is the ability to combine uniquely principles, procedures, and declarative knowledge previously learnt, to solve problems never encountered before . This yields new learning as learners are able to tackle problems of similar nature more easily in the future. The key factor behind problem solving in is the knowledge of principles and procedural rules in a specific domain and how these relate to each other. The first stage of problem-solving is problem representation. This needs problem decomposition and identifying the appropriate problem schemata. A skilled problem solver maps the current to a previous problem situation. The learner maps the attributes of the current problem to the important features of the problem stored in long-term memory. If this mapping is through meaningful conceptual links, this would facilitate easy problem solving (Savery, 2006) The next stage of problem-solving is Solution Planning. This involves searching, picking, combining and serializing the relevant knowledge. Then comes Solution implementation where learners identify and apply appropriate principles to find a solution. Finally in Solution evaluation the learner verifies that the solution is correct (Smith, 2005; Sergienko, 2002; Zhang).
Instructional Design and Strategy For Instructional Design of any course, there must be: 1. 2. 3. 4. 5. 6.
Needs analysis Analysis of learners and learner characteristics Determination of Pre requisites Formation of Learning Objectives and Designing assessments Instructional Strategy Implementation and Evaluation
Needs, learners and their characteristics analysis are first completed. Then, prerequisite analysis is performed. This is followed by the framing of the learning objectives and the design of assessments for evaluating learner performance. Next comes, instructional strategy design and implementation and finally its evaluation for efficacy.
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Our main focus is on the design of a model for Unified Instructional Strategy integrating all relevant types of instructional strategies in conjunction with the integrated architecture of human memory and learning.
Design of the Unified Instructional Strategy Model The design of the model for Unified Instructional Strategy involves a five stage approach: 1. 2.
Review of relevant learning already present in the long-term memory of the learner Introduction of new concepts and associating them to already existing schemata in semantic memory Introduction of new principles and associating them with learner’s existing knowledge schemata and introduction of new procedures and their association. Exposure of a wider perspective to concepts, principles and procedures through a wide range of illustrative scenarios enhanced by experiential and inquiry based learning, thus strengthening and deepening long-term memory schemata Exposure of learners to problem-solving situations which apply the concepts, principles and procedures in the new learning, which ensures learning is cemented in long-term memory
3. 4.
5.
Let us now consider each stage of the model. Together we illustrate with a case study how our model can be applied to each stage. Our case study is on computer networking to comprehend how an Ethernet Network operates and to study its applications. Prerequisite:(as relevant to the learning objectives): How a computer functions Learning Objectives of the Case Study: 1. 2. 3. 4. 5. 6.
Describe the properties of a Network Interface Card (NIC) Comprehend the Ethernet Address format and the IP Address of NIC List the fields in the Ethernet frame Comprehend and apply how the Ethernet Address and IP address are mapped Describe and Study the use of Address Resolution Protocol (ARP) Comprehend how systems in one network and across different networks communicate through the NIC.
Stage 1: Review In any instruction design the introduction should prepare the learners by stimulating their attention and making available relevant memory schemas in working memory. This existing knowledge makes the new information easier to grasp. The learners should have a clear idea of the learning objectives, their purpose and usefulness. Side by side the learning activities should be previewed. Judicious review should make available for working memory the schemata which will be used to correlate and associate the new learning so as to extend them with the fresh concepts,
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principles and procedures learnt. The review process is extremely important in aiding and enhancing learning. It would be more efficacious if at the appropriate points in the instruction a strategy for refreshing the relevant schema is used. With reference to our Case Study and the pre-requisite of how a computer functions, consider this chunk of long-term memory schema:
Fig 2: Schema in Long –Term Memory Fig. 2 depicts how people communicate by talking and messaging using mobiles. It also illustrates the fact that people typically communicate data through wired networks. All learners having the prerequisites would already be knowing this, they only need to be reminded of it. By review if this structure is activated, the working memory will have access to it. So it becomes much easier to link in the new learning regarding Ethernet communication to the existing knowledge available as an activated chunk.
Stage 2: Introduction of New Concepts The second stage of our Unified Instructional Strategy is the introduction of new concepts. A concept as defined earlier is a set of objects, events or symbols which have an association. New concepts should always be introduced in the context of existing concepts and principles. Considering our Ethernet Case Study the concepts which would need to be learnt are: Ethernet Frame, Ethernet Address, Fields of Ethernet Frame, IP Address, ARP(Address Resolution Protocol), unicast, multicast and broadcast frames Suppose we want to introduce the concept of a message as an Ethernet frame. First, we could review the existing knowledge of a frame by showing images or discussing a photograph or door frame. Once the learner recognizes that a frame acts as a container, he or she can be exposed to the concept of a message and how messages may be sent on mobiles or through computers on Ethernet.
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Then the fact that the message needs to be put in a container is driven home, like a letter in an envelope or the data in an Ethernet Frame. Then we would like to expose the learner to the fields of an Ethernet frame including the address fields in the frame. To do this we could recall that a letter has an envelope which contains the destination address, the source address (From Address) and the message itself. Similarly the Ethernet Frame has the destination address, the source address in the header or initial part of the frame and the messsage in the body of the frame. When concepts are introduced in this manner it becomes easy for the learner to associate this into his or her existing schemata as illustrated in Fig 3. The colored ellipses and boxes are concepts which learners already know and recalled through review. The white ellipses and boxes are the new learning of Ethernet networks which is associated to the existing learning. The remaining concepts can be introduced in similar fashion.
Fig 3: Introducing and Linking a Concept using an Ethernet Frame as Example Needless to say concepts can be introduced and associated with a combination of direct and experiential learning techniques (Lewis, 2003).
Stage 3: Introduction of New Principles and Procedures Principles relate two or more concepts, and are depicted by IF THEN or Cause and Effect relationships. For our case study on Ethernet networks the principles which would need to be covered are:
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1. 2. 3. 4.
5. 6.
A Network Interface Card (NIC) will accept a unicast frame only if the destination address in frame is same as the NIC’s Ethernet address. All systems in same Network will have the same prefix in the IP Address. The IP packet with the application message will be encapsulated in a Frame. If a system finds the destination IP address in its ARP cache it will use the corresponding Ethernet address as the destination address field in its frame, else it will broadcast an ARP and wait for the reply. A system receiving a broadcast ARP will reply with its Ethernet address to sender only if the IP address in the ARP broadcast frame corresponds to its own IP address. If the destination IP address has a prefix different from that of the sending system it sends the frame to the gateway router on the network.
The first principle underlying the receipt of Ethernet frames by a NIC can be stated thus: A computing system in an Ethernet network will receive and process a frame only if the destination address in frame matches the address of the Network card in the computing system receiving the frame. So the concepts which must be known to understand this principle are: 1. 2. 3.
Computer systems communicate on Ethernet networks by sending Ethernet frames which have the destination and source address. Every computing system on an Ethernet network has a network card which receives and transmits Ethernet frames. It has a fixed Ethernet address. An Ethernet card receiving frames will process only those whose destination addresses is same as card’s own address.
This principle will get associated as a production in memory by the following: IF the network card in a computing system receives an Ethernet frame AND IF the destination address in frame is equal to the ethernet address of the card THEN it will process the frame ELSE it will discard the frame. Here we have for simplicity assumed that all frames are unicast. This method of presenting concepts and principles, associating them in the way described mimics the way information is stored in memory. So the learner will find it very easy to absorb such learning and recall when needed. Learning of procedures is also equally important. Procedures are stored as a series of steps in procedural memory. The procedure may have decision steps too. Hence a procedure consists of recognition of the occurrence of a concept or concepts in a scenario and applying the appropriate procedural steps. Our case study would involve the learning of the following procedures. 1. 2.
How to determine Ethernet address using Address Resolution Protocol (ARP) Cache. How to connect the systems to switch
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3. 4. 5.
How to connect more than 1 switch How to test the connectivity between the systems How to troubleshoot
Let us consider the first procedure of how to determine the Ethernet address using the ARP cache . Typically every Network Card has an Ethernet address as well as a logical IP address. When a system wants to transmit a message it determines the destination IP address first. Then it searches for the Ethernet address in a cache called an Address Resolution Protocol (ARP) cache. So the procedure may be depicted thus: IF a system wants to send a message to a destination: 1. 2.
Get the IP address of destination Look up the ARP cache for the IP Address and corresponding Ethernet address of destination. IF found use the Ethernet address as destination address and own NIC Ethernet address source address
3.
Form the frame with message. Transmit the frame. ELSE broadcast an ARP request and wait for reply. It is extremely important the learning be logically presented as concepts, principles and procedures, which will immensely enhance learner absorption and retention.
Stage 4: Give Wider Perspectives to the Concepts and Principles/Procedures Learnt The fourth stage is to give a broader base to the learner. To achieve this it is necessary to design a range of experiential activities for the learner so that the knowledge he has acquired is cemented into his long-term memory. This will also help in deepening the links and associations forged in the learner. Experiential activities would be scenario based, and could be role plays/discussions/assignments/laboratory/hands-on assignments as appropriate. There is no sacrosanct sequencing advocated; it is the task of the instructional designer to determine the most optimum flow for the learner. It is important to make the learner view the concepts, principles and procedures from different angles. Our case study could use the following for broadening the perspectives of the learners. 1. 2. 3. 4.
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Demonstrate how to examine the IP and Ethernet address of system. Let participants check the IP/Ethernet address and verify. Let participants connect to a switch and use ping command to check connectivity. Demonstrate how ARP functions using ARP command, and let the participants verify. Demonstrate with ethereal( a packet capturing tool) and let participants check IP address, Ethernet address and ARP request and response.
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In the case study considered, assignments can be designed to make the learner reason why the destination address should be the first field, what happens when a frame reaches a switch, why a logical IP Address and Ethernet address are both necessary, why should an ARP be broadcast and so on. The intent here is to thoroughly analyze the knowledge gained be it concepts, procedures or principles. Needless to say, analogies have to be drawn to prior knowledge. It is the instructional strategy designer’s imagination which needs to be stretched to the limit to produce content to make the learner truly appreciate and relate to his acquired knowledge. The fruit of the instructional strategist’s design will be the strength and longevity of the structures created and linked in the long-term memory of the learner.
Stage 5: Expose the Participants to a Problem Solving Situation which Applies the New Learning The fifth and final stage is to expose the learners to a problem solving situation which makes them apply their learning. This could be one problem scenario or more. The essence of this stage is to crystallize the knowledge gained in the previous four stages by exposing learners to a practical situation which makes them appropriately recall and apply the knowledge acquired. This problem solving exercise clarifies any knowledge in the learner’s mind which is a bit fuzzy. The role of the instructor in guiding the learners is of paramount importance. Now for our case study introduce problems in the network like faulty network card, or faulty Port of switch or faulty switch and let learners troubleshoot and diagnose the problem. In the problem solving scenario of troubleshooting the faulty network the fault generated could be faulty IP addressing or problem in the cable or problem in the Network Card or combinations of these. Troubleshooting, diagnosing and rectifying the problem in the network using a logical sequence of identifying the concepts, principles and procedures studied for application to the problem resolution helps to make the learner’s learning much more concrete and lasting in long-term memory. Collobaration with other learners in a group in the problem solving exercise also enhances learning.
Session Conclusion and Summary Have a group discussion to discuss the learning and share ideas and comments and the problems typically faced and solved and how the principles and concepts learnt helped them to solve the problem to embed learning into long-term Memory.
Conclusion The Unified Instructional Strategy marries direct learning, inquiry based and experiential learning with the architecture of human memory into an integrated instructional model which serves as a powerful means to impart learning. The Framework helps cement and merge new knowledge seamlessly into existing learning. Moreover, this embedded and strongly associated learning is fused into long-term memory through a range of associations. Hence learners will be able to recognize, tackle and resolve hitherto unencountered problems by drawing on their rich repertoire of knowledge. This
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model is however no philosopher’s stone; it requires a lot of thought and effort from the instructional strategy designer. We are of the strong opinion that our unified model for instructional strategy can prove to be a very powerful and efficient methodology for learning.. It can be utilized to dramatically increase the absorption power and competency gains of the learner. We are in the process of implementing this model in our organization.
References Anderson, J. R., Reder, L. M., & Lebiere, C. (1996). Working Memory: Activation Limitations on Retrieval. Cognitive Psychology, 30. 221-256. Byrnes, P. J. (2001). Minds, Brains and Learning: Understanding the Psychological and Educational Relevance of Neuroscientific Research. New York: The Guilford Press. Concept Learning (n.d.). Retrieved from http://dspc11.cs.ccu.edu.tw/ml93/lecture3-concept-learning.pdf Dehn, M. J. (2008). Working Memory and Academic Learning: Assessment and Intervention. New Jersey: John Wiley and Sons, Inc. Inquiry-Based Learning. (n.d.). Retrieved from http://en.wikipedia.org/wiki/Inquiry-based_learning Kahn, P., & O’Rourke, K. (2005). Understanding Enquiry-Based Learning. In Barrett, T., Mac Labhrainn, I., Fallon, H. (Eds), Handbook of Enquiry & Problem Based Learning. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance during Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist. 41(2). Kuhn, D. (2007). Is Direct Instruction an Answer to the Right Question? Educational Psychologist, 42(2). 109-113. Lewis, R. L. (2003). Learning in ACT-R: Chunking Revisited. Retrieved February 2, 2010, from http://cll.stanford.edu/symposia/cogarch/cogarch-lewis.pdf Lewis-Peacock, J. A., & Postle, B. R (2008). Temporary activation of Long-Term Memory Supports Working Memory. The Journal of Neuroscience, 28(35). 8765-8771 Merrill, D. M., & Tennyson, R. D. (1977). Teaching Concepts: An Instructional Design Guide. New Jersey: Educational Technology Publications, Inc. Principle Learning (n.d). Retrieved from http://www.angelfire.com/bug/idt/principle.htm Problem-Based Learning (n.d.). Retrieved from http://en.wikipedia.org/wiki/Problem-based_learning Savery, J.R., (2006). Overview of Problem-Based Learning: Definitions and Distinctions. Interdisciplinary Journal of Problem-based Learning. 1(3). Sergienko, G. (2002). Using Instructional Design to Improve Student Learning. Journal of the Association of Legal Writing Directors. 1. Smith, P. L., & Ragan T.J, (2005). Instructional Design (3rd Edition). New Jersey: John Wiley and Sons, Inc. Venkatesan, S., & Fragomeni, S. (2008). Evaluating learning outcomes in PBL using fuzzy logic techniques. In Proceedings of the 2008 AaeE Conference, Yeppoon. Wilson, B. J. (1995). Maintaining the ties between Learning theory and Instructional Design. Retrieved from http://carbon.ucdenver.edu/~bwilson/mainties.html. Zhang, C. (n.d.). Strategies for Problem-solving Instruction. Retrieved from http://www.slideshare.net/mlandis/strategies-for-problemsolving-instruction
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About the Authors Jayasurya Venugopalan Jayasurya Venugopalan graduated from IIT Madras, with a B.Tech (Electronics) and subsequently did his MS from BITS Pilani. He has a total work experience of 29 years , the first 9 years as an Instrument Engineer in a paper manufacturing industry. Subsequently he was in the academic profession teaching in the computer Science Department of several engineering colleges. Before joining Wipro Technologies in 1999 he was the Asst. Prof and HOD of the CSE Dept of BMS College of Engineering, a premier engineering college in Bangalore. In Wipro he has been with the Training Division called Talent Transformation and has been conducting courses both instructor led and online in various topics in Networking, Network Management and Operational Support Systems. Over the last year he has been associated with the School of Talent Transformers within Talent Transformation where he is actively doing research in curriculum development and Teaching methodologies. Dr. Annapoorna Gopal Dr. Annapoorna Gopal has completed her research in the area of Human Resources from SIBM, Pune, India. She heads the School for Talent Transformers at Wipro and is working with Wipro for the last nine years. Prior to this she was associated as a Senior Faculty in the Post Graduate Department of Christ College, Bangalore.At Wipro Annapoorna has adorned multiple roles both in the delivery and managerial capacities alike. She has contributed to the development of competency on Rational software development tools, managed the assessment center, introduced different teaching methodologies with specialization in the “case Based Approach to Training”, championed Distributed Learning projects and has designed and delivered a course on Wipro Values. As the head of Academic Initiatives Annapoorna took care of the Wipro Academy of Software Excellence which is the country’s largest workintegrated collaborative learning program. In her current role, Annapoorna and her team work towards enabling Talent Transformation at Wipro move up the value chain. She has published several papers and is a regular contributor of articles in the press
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