Computer-Based Instruction

Computer-assisted instruction was first used in education and training during the 1950s. Early work was done by IBM and such people as Gordon Pask, and O.M. Moore, but CAI grew rapidly in the 1960s when federal funding for research and development in education and industrial laboratories was implemented. The U.S. Government wanted to determine the possible effectiveness of computer-assisted instruction, so they developed two competing companies, (Control Data Corporation and Mitre Corporation) who came up with the PLATO and TICCIT projects. Despite money and research, by the mid seventies it was apparent that CAI was not going to be the success that people had believed. Some of the reasons are: CAI had been oversold and could not deliver, lack of support from certain sectors, technical problems in implementation, lack of quality software, and high cost. Computer-assisted instruction was very much drill-and-practice - controlled by the program developer rather than the learner. Little branching of instruction was implemented although TICCIT did allow the learner to determine the sequence of instruction or to skip certain topics.

 

 

 

 

Contract Learning

Contract learning involves the use of contingency contracts, which define the terminal behavior the student is to achieve and conditions for achievement and consequences for completion or non-completion of the assigned task(s). The contingency contract is mutually agreed upon by teacher and student after negotiations. Contract learning is often used in open educational systems in which students from various grade levels share in learning activities.

Contract learning can also be useful in a college setting. According to Knowles, (1991, p. 39) "Contract learning is, in essence, an alternative way of structuring a learning experience: It replaces a content plan with a process plan." For more information on the concept of contract learning and adult learning theory, visit http://www.msu.edu/user/coddejos/contract.htm.

Codde, J. R. (1996). Using learning contracts in the college classroom. Retrieved August 19, 2002, from Michigan State University, Educational Technology Certificate Program Web site: http://www.msu.edu/user/coddejos/contract.htm

Driscoll, M. (2000). Psychology of learning for instruction. Needham Heights, MA: Allyn & Bacon.

 

 

 

 

Individualized Instruction

Similar to programmed learning and teaching machines individualized instruction began in the early 1900s, and was revived in the 1960s. The Keller Plan, Individually Prescribed Instruction, Program for Learning in Accordance with Needs, and Individually Guided Education are all examples of individualized instruction in the U.S. (Saettler, 1990).

 

 

 

 

Programmed Instruction

After experimental use of programmed instruction in the 1920s and 1930s, B. F. Skinner and J.G. Holland first used programmed instruction in behavioral psychology courses at Harvard in the late 1950s. Use of programmed instruction appeared in elementary and secondary schools around the same time. Much of the programmed instruction in American schools was used with individuals or small groups of students and was more often used in junior high schools than senior or elementary schools (Saettler, 1990). Early use of programmed instruction tended to concentrate on the development of hardware rather than course content. Concerned developers moved away from hardware development to programs based on analysis of learning and instruction based on learning theory. Despite these changes, programmed learning died out in the later part of the 1960s because it did not appear to live up to its original claims (Saettler, 1990).

 

 

 

 

System Approach

The systems approach developed out of the 1950s and 1960s focus on language laboratories, teaching machines, programmed instruction, multimedia presentations and the use of the computer in instruction. Most systems approaches are similar to computer flow charts with steps that the designer moves through during the development of instruction. Rooted in the military and business world, the systems approach involved setting goals and objectives, analyzing resources, devising a plan of action and continuous evaluation/modification of the program. (Saettler, 1990)

 

 

 

 

Collins & Stevens Inquiry Teaching Model

Collins' Cognitive Theory of Inquiry Teaching is a prescriptive model, primarily Socratic in nature, meaning that it relies upon a dialectic process of discussion, questions and answers that occurs between the learner and instructor. The process is guided in order to reach the predetermined objectives, which are described in this theory as teacher goals and subgoals. Ultimately, the learners will discover "how to learn".

Teacher goals and subgoals is one of three main portions of Collins' theory. The second is the strategies used to realize said goals and subgoals and the third is the control structure for selecting and pursuing the different goals and subgoals.

Brooks , C. E. (n.d.). Cognitive theory of inquiry teaching. Retrieved September 10, 2002, from University of Arkansas, Educational Technology Department Web site: http://comp.uark.edu/~brooks/cognitive.html

 

 

 

 

Events of Instruction/Conditions of Learning

Events of Instruction

Preparation for Learning
1. Attending - gain learner's attention
2. Expectancy - inform learner of objectives
3. Retrieval - recall relevant information and/or skills to working memory or stimulate recall of prior learning.

Acquisition and Performance
4. Selective perception - remembering stimulus features, distinctive features.
5. Semantic encoding - provide learning guidance
6. Retrieval and responding - elicit a performance
7. Reinforcement - provide informative feedback

Retrieval and Transfer
8. Cueing retrieval - assess performance
9. Generalizing - applying learning to a new situation

Barba, R. (1997). Events of instruction (Robert Gagne). Retrieved September 10, 2002, from San José University, College of Education Web site: http://www.sjsu.edu/depts/it/edit186/gagne.html

 

 

 

 

Information Processing Model

This model represents information processing as a computer model. Information processing easily relates to computer input-process-output. Processing information involves subroutines or procedures. Subroutines are performed in a hierarchical manner to complete tasks. Flow of control can be diagrammed. Logic Theorist was a computer program by Newell, Shaw and Simon (1955-60) used to simulate the human process of solving theorems in symbolic language. At the same time MIT, had a pattern recognition program.

There have been many computer models for human information processing. Two types of information processing models are those dealing with simulation, or step by step, and those that are dealing with artificial intelligence and are task driven. Logic Theorist emulated six human characteristics of problem-solving behavior.

Information process theory of learning. (n.d.). Retrieved September 10, 2002, from University of Missouri in Columbia, The College of Education Web site: http://tiger.coe.missouri.edu/~t377/IPTheorists.html

 

 

 

 

Keller's ARCS Model of Motivation

John M. Keller proposed four conditions that must be met for a learner to be motivated to learn. Attention, relevance, confidence, and satisfaction (ARCS) are the conditions that, when integrated, motivate someone to learn. Moreover, Keller suggests that the ARCS conditions occur as a sequential process (Driscoll, 1993, p. 312). The conditions should be sustained to keep the learner interested in the topic. Once a learnerís attention is lost, motivation is lost, and learning does not occur. Shneiderman (1998, p. 25) states that "memorable educational experiences are enriching, joyful, and transformational." Motivation theory argues that relevant phenomena fulfill personal needs or goals, which enhances effort and performance (Means, Jonassen, & Dwyer, 1997). How then can one ensure that the ARCS model remain active? The key is to vary the conditions to engage the learner. Because each component of Kellerís ARCS model builds upon the next model, the instructor should keep the four components in mind when designing instruction.

Driscoll, M. (1993). Psychology of learning for instruction. Needham Heights, MA: Allyn & Bacon

Fernández, J. (1999). Attribution theory and Keller’s ARCS model of motivation. Retrieved September 10, 2002, from George Mason University, Graduate School of Education, Instructional Design & Development Immersion Program Web site: http://chd.gse.gmu.edu/immersion/knowledgebase/strategies/cognitivism/keller_ARCS.htm

Means, T., Jonassen, D., Dwyer, F. (1997). Enhancing relevance: Embedded ARCS strategies vs. purpose. Educational Technology Research and Development, 45, 5-17.

Shneiderman, B. (1998). Relate—Create—Donate: A teaching/learning philosophy for the cyber-generation. Computers & Education, 31, 25-39.

 

 

 

 

Merrill's Component Display Model

CDT specifies how to design instruction for any cognitive domain. CDT provides the basis for the lesson design in the TICCIT computer based learning system (Merrill, 1980). It also was the basis for the Instructional Quality Profile, a quality control tool for instructional materials (Merrill, Reigeluth & Faust, 1979). Fo example, if we were designing a complete lesson on equilateral triangles according to CDT, it would have the following minimum components:

Objective - Define an equilateral triangle (Remember-Use)
Generality - Definition (attributes, relationships)
Instance - Examples (attributes present, representations)
Generality Practice - State definition
Instance Practice - Classify (attributes present)
Feedback - Correct generalities/instances
Elaborations - Helps, prerequisites, context

If the generality was presented by an explanation or illustration, followed by practice examples, this would be an expository strategy (EG, Eeg). On the other hand, if the students were required to discover the generality on the basis of practice examples, this would be an inquisitory strategy (IG, Ieg).

Merrill, M.D. (1980). Learner control in computer based learning. Computers and Education, 4, 77-95.

Kearsley, G. (n.d.). Component display theory (M.D. Merrill). Retrieved September 7, 2002, from Explorations in Learning & Instruction: The Theory Into Practice Database Web site: http://home.sprynet.com/~gkearsley/tip/merrill.html

 

 

 

 

Action Learning

Reg Revans is considered the architect of action learning. Inglis (1994) defined AL as "a process which brings people together to find solutions to problems and, in doing so, develops both the individuals and the organization" (p. 3). According to Spence (1998), Revans loosely defined action learning as the process of learning "from and with peers while tackling real problems (O'Neil and Marsick 1994)" (p. 1). However, Revans (1980) also says that it is not just project work, job rotation, case studies or business games. According to Inglish (1994), action learning differs from these other methodologies in the following ways.

Problem, set, client, set advisor, and process are the basic elements of action learning. The following is a brief explanation of each element (Spence, 1998).

Action learning has been applied in many areas of adult education such as nursing education and human resource development graduate programs.

Inglis, S. (1994). Making the Most of Action Learning. Aldershot, England: Gower.

O'Neil, J., and Marsick, V. J. (1994). Becoming critically reflective through action reflection learning TM. New Directions for Adult and Continuing Education no. 63, 17-29. (EJ 494 200).

Revans, R (1980). Action learning: New techniques for management. London: Blond & Briggs.

Spence, J. (1998). Practice application brief: Action learning for individual and organizational development [Electronic version]. (Developed with funding from the Office of Educational Research and Improvement, National Library of Education, U.S. Department of Education, under Contract No. RR93002001). Retrieved August 24, 2002, from http://ericacve.org/docs/pab00009.htm

 

 

 

Anchored Instruction

Anchored instruction is a major paradigm for technology-based learning that has been developed by Cognition & Technology Group at Vanderbilt (CTGV) under the leadership of John Bransford. While many people have contributed to the theory and research of anchored instruction, Bransford is the principal spokesperson and hence the theory is attributed to him.

The initial focus of the work was on the development of interactive videodisc tools that encouraged students and teachers to pose and solve complex, realistic problems. The video materials serve as "anchors" (macro-contexts) for all subsequent learning an d instruction. As explained by CTGV (1993, p52): "The design of these anchors was quite different from the design of videos that were typically used in education...our goal was to create interesting, realistic contexts that encouraged the active construct ion of knowledge by l earners. Our anchors were stories rather than lectures and were designed to be explored by students and teachers. " The use of interactive videodisc technology makes it possible for students to easily explore the content.

Anchored instruction is closely related to the situated learning framework (see CTGV, 1990, 1993) and also to the Cognitive Flexibility theory in its emphasis on the use of technology-based learning.

CTGV (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 19(6), 2-10.

CTGV (1993). Anchored instruction and situated cognition revisted. Educational Technology, 33(3), 52- 70.

Kearsley, G. (n.d.). Anchored Instruction (John Bransford & the CTGV). Retrieved September 7, 2002, from Explorations in Learning & Instruction: The Theory Into Practice Database Web site: http://tip.psychology.org/anchor.html

 

 

Authentic Learning

Authentic learning refers to the idea that learners should be presented to problems that are realistic situations and found in everyday applications of knowledge (Smith & Ragan, 1999). Authentic learning is the type of learning promoted by anchored instruction, in which instruction is "anchored" in a realistic problem situation (Cognition and Technology Group, 1990).

Young (1993), recommends the following test of "authenticity." Learning situations should include some of the characteristics of real-life problem solving, including ill-structured complex goals. There should also be an opportunity to distinguish between relevant and irrelevant information. Finding and defining problems as well as solving them should be a generative process. Finally, students should engage in collaborative activities in which they draw upon their beliefs and values.

For more information on Authentic Learning, visit Tiffany Mara's (University of Michigan, Ann Arbor) Web site, http://www-personal.umich.edu/~tmarra/authenticity/authen.html.

Cognition and Technology Group. (1990). Anchored instruction and its relationship to situated cognition. Educational Researcher, 19(8), 2-10.

Smith, P. and Ragan, T. (1999). Instructional design (2nd ed.). New York: John Wiley & Sons, Inc.

Young, M. (1993). Instructional design for situated learning. Educational Technology Research & Development, 41(1), 43-58.

 

 

 

 

Case-Based Learning

Case-based learning using case studies to present learners with a realistic situation and require them to respond as the person who must solve a problem (Smith & Ragan, 1999). In order to solve problems, learners select and manipulate several principles. According to Hudspeth and Knirk (1989),

A complete case describes an entire situation and includes background information, the actions and reactions of persons involved, the solution, and the possible consequences of the actions taken. Case materials should have enough background information and detail to that they are readable and believable (p. 31).

Case-based learning is appropriate for learning to problem solve when there is no one correct solution, particularly with more complex ill-structured problems (Smith & Ragan, 1999). Case studies can be written so that learners use more cognitive strategies as they proceed through increasing levels of instruction. Cases were traditionally used in professional education to teach decision making skills, such as the Harvard Business School case approach. Use of case-based studies has also become widespread in the field of medical education.

For an example of investigative case-based learning in biology, visit http://www.bioquest.org/case99.html.

Hudspeth, D., & Knirk, F.G. (1991). Case study materials: Strategies for design and use. Performance Improvement Quarterly, 2(4), 2.

Smith, P. and Ragan, T. (1999). Instructional design (2nd ed.). New York: John Wiley & Sons, Inc.

 

 

 

 

Cognitive Apprenticeship

The focus of this learning-through-guided-experience is on cognitive and metacognitive skills, rather than on the physical skills and processes of traditional apprenticeships. Applying apprenticeship methods to largely cognitive skills requires the externalization of processes that are usually carried out internally. Observing the processes by which an expert listener or reader thinks and practices these skills can teach students to learn on their own more skillfully (Collins, Brown, Newman, 1989, p. 457-548). This method includes:

  1. Modeling - involves an expert's carrying out a task so that student can observe and build a conceptual model of the processes that are required to accomplish the task. For example, a teacher might model the reading process by reading aloud in one voice, while verbalizing her thought processes (summarize what she just read, what she thinks might happen next) in another voice.
  2. Coaching - consists of observing students while they carry out a task and offering hints, feedback, modeling, reminders, etc.
  3. Articulation - includes any method of getting students to articulate their knowledge, reasoning, or problem-solving processes.
  4. Reflection - enables students to compare their own problem-solving processes with those of an expert or another student.
  5. Exploration - involves pushing students into a mode of problem solving on their own. Forcing them to do exploration is critical, if they are to learn how to frame questions or problems that are interesting and that they can solve (Collins, Brown, Newman, 1989, 481-482).

 

 

 

 

Cognitive Flexibility Hypertext

Cognitive Flexibility Hypertext (CFH) is a hypermedia learning environment that provides users with several nonlinear paths of traversing content through the use of cases, themes, and multiple perspectives. A CFH supports exploration of an ill-structured (ill-defined) knowledge domain through multiple representations of the content, promoting flexible knowledge acquisition to enhance transfer to real-world contexts (Jonassen, Dyer, Peters, Robinson, Harvey, King and Loughner, 1997). The WWW is an ideal medium for designing CFH due to its hyperlinking feature and access to widespread resources that add richness to content.

 

 

 

 

Collaborative Learning

Collaborative learning, also called cooperative learning, is heavily emphasized in most constructivist approaches (Roblyer, Edwards, & Havriluk, 1996). Actually, students working in groups to solve problems achieves many goals that supporters of both constructivism and directed instruction consider to be important. The CTGV finds that collaborative learning is the best way to promote generative learning.

Perkins (1991) finds that collaborative learning demonstrates the notion of distributive intelligence, which states that accomplishment is not a function of one person, but rather a group in which each contributes to the achievement of desired goals. Cooperative learning is an ideal way for students to learn the skills that extend beyond the classroom of sharing responsibility and working together toward common goals. According to Driscoll (2000), collaboration also provides students with a way to understand point of view outside their own. Advances in technology over the past several years have made computer-supported collaborative learning possible. Web-based technologies can make thinking more visible through virtual access to knowledge experts.

Driscoll, M. (2000). Psychology of learning for instruction. Needham Heights, MA: Allyn & Bacon.

Perkins, D. (1991). Technology meet constructivism: Do they make a marriage? Educational Technology, 31(5), 18-23.

Roblyer, M.D., Edwards, J. & Havriluk, M.A. (1996) Learning Theories and Integration Models (Chapter 3). In Roblyer, Edwards, & Havriluk, Integrating educational technology into teaching. Prentice Hall.

 

 

 

 

Communities of Practice

Communities of Practice include learners and instructors who interact with one another and other experts via virtual spaces, to build a reciprocal interchange of ideas, data, and opinions. Transformative styles of communication are characteristic, where the contributor, participator and the lurker or receiver, are "changed" as they share in the goal of learning and knowledge generation and application (Wilson & Cole, 1996).

 

 

 

 

Computer-Supported Intentional Learning Environments (CSILEs)

Essentially, CSILE is a type of computer conference in which learners create communal databases entirely through person-to-group rather than person-to-person communication. CSILE is a collective knowledge building effort that requires students to do planning, goal setting and problem solving. CSILEs are based on a specific environment developed at the Ontario Institute for Studies in Education.

 

 

 

 

Discovery Learning

Discovery learning has various definitions. At one end of the spectrum we find discovery learning in its simplest form. The tools and information needed to solve a problem or learn a concept are provided and the learner "makes sense" of them. Another definition is discovery learning as experimentation with some extrinsic intervention -- clues, coaching, and a framework to help learners get to a reasonable conclusion. At the other end of the continuum is the expository teaching model of discovery learning where the learner "discovers" what the teacher decides he is to discover using a process prescribed by the teacher.

Bardin, D. (1999). Discovery learning. Retrieved September 10, 2002, from San Diego State University, Encyclopedia of Educational Technology Web site: http://coe.sdsu.edu/eet/Articles/discoverylearn/start.htm

 

 

 

 

Distributed Learning

Distributed Learning is when learning is distributed across space, time, and various media. When telecommunications media is utilized, distributed learning refers to off-site learning environments where learners complete courses and programs at home or work by communicating with faculty and other students through e-mail, electronic forums, videoconferences, and other forms computer-mediated communication and Internet and Web-based technologies. Distributed learning environments "result in a diffuse sense of cognition - where what is "known" lies in the interaction between individuals and artifacts, such as computers and other technological devices (Dabbagh & Bannan-Ritland, in preparation).

Dabbagh, N. & Bannan-Ritland, B. (in preparation - under contract). Online learning and course management systems: Concepts, strategies, and application. Upper Saddle River, NJ: Prentice Hall, Inc. Expected Publication date, January 2003.

 

 

 

 

Epistemic Games

Epistemic games are a formalized structure learning communities use to create knowledge. Conventions are set that represent defined cultural patterns or forms. Working together to generate these forms is called participating in epistemic games. The game involves creating rules or conventions to be followed in generating a given epistemic form. The products of working together are called epistemic forms. "Completed" forms contain new knowledge and adhere to defined structures accepted by the community (Collins & Ferguson, 1993; Morrison & Collins, in press).

 

 

 

 

Generative learning

Generative learning is a learning process in which learners are given an overall problem and are asked to generate sub-problems, subgoals, and strategies in order to achieve the larger task (Duffy & Jonassen, 1992). Generative learning strategies can be divided into four major stages: (1) recalling information from long-term memory; (2) integrating new knowledge with prior knowledge; (3) relating prior knowledge to new concepts and ideas in a meaningful way; and (4) connecting new materials to information or ideas already in the learner's mind (Generative Learning, 2000). Using this strategy, a learner relates new ideas to prior knowledge in order to provide meaning to the new material (Ryder, 1998).

 

 

 

 

Goal-Based Scenarios (GBSs)

GBSs offer learners the opportunity to role-play from a certain "character's" perspective or point of view. Their "goal" is for the learner to accomplish a mission or task associated with their role in the scenario. In order to achieve this goal, the learner needs to acquire particular skills and knowledge. This is where and when learning takes place. A GBS, therefore, serves both, to motivate learners, and to give them the opportunity to "learn by doing." "As long as a goal is of inherent interest to learners, and the skills needed to accomplish those goals are the targeted learning outcomes, [we] have a match and a workable GBS (Naidu, 2001, p.2)."

A designer of a GBS tends to look at it from the top-down. What drives the design of a GBS is the set of target skills the designer wishes the student to gain in the GBS. A student, on the other hand, tends to look at a GBS from the bottom-up. What drives a student is the context and structure of the activities the GBS offers.

 

 

 

 

Inquiry-based Learning

Inquiry-based learning is an approach to instruction that engages students in investigations to satisfy curiosities. Curiosities are satisfied when individuals construct mental frameworks that adequately explain their experiences (Haury, 1993). The learner's involvement in the learning content fosters skills and attitudes that permit the learner to seek resolutions to questions and issues while constructing new and meaningful knowledge (Inquiry-based Learning: Explanation, 2001, April).

 

 

 

 

Microworlds/Simulations

In microworlds, students test 'What do you think will happen if…?' questions in "…constrained problem spaces that resemble existing problems in the real world (Jonassen, 1996, p.237)." Learners generate hypotheses as they use their knowledge and skill to guess what will happen, try out those guesses, and reformulate them based on the results of their actions within the microworld. Microworlds provide the learner with the observation and manipulation tools necessary to explore and test. The key idea behind microworlds is creating an environment in which students explore the ideas being learned (Jonassen, 1996).

Simulations are similar to microworlds in that they are experiential and model reality. Simulations "…range from models that mirror the simplified essence of reality to elaborate synthetic environments with immersion interfaces that place students inside alternate virtual worlds (Dede, 1996, p.14)." Microworlds differ from simulations in that microworlds are structured to match the user's cognitive level so that it is appropriate to the users needs and level of experience (Rieber, 1992).

 

 

 

 

MOOs and MUDs

MOO is an acronym for MUD, Object-Oriented. MUD stands for Multi-user Dimension, or Dungeon, reflecting its origin as a form of the Dungeons and Dragons game developed for multi-users on the Internet. "In Web-based learning, simulated role portrayal can be facilitated through Multi-User Dialogue (MUD) environments, in which instructors create a multi-user space with a central theme, characters and artifacts (Khan, 2001, p.81, in Walker, 1997)."

Most MUDs still retain this game-like atmosphere, with players earning levels often by shooting and killing other players. MOOs, however, developed as more social spaces, lending themselves readily to use as a virtual classroom, or as spaces for conferences and meetings.

Walker, J. (1997, revised 2001). Workshop on synchronous communication in the language arts classroom. Retrieved September 10, 2002, from Georgia Southern University, Department of Writing and Linguistics Web site: http://www2.gasou.edu/facstaff/jwalker/tutorials/cte.html

 

 

 

 

Problem-Based Learning (PBL)

PBL engages the learner in a problem-solving activity. In this process, instruction begins with a problem to be solved rather than content to be mastered (Hsiao, 1996). Students are introduced to a real-world problem and are encouraged to dive into it, construct their own understanding of the situation, and eventually find a solution (Grabowski, Koszalka, & Mccarth, 1998). Major goals of PBL are to help students develop collaborative learning skills, reasoning skills, and self-directed learning strategies (Hsiao, 1996).

Five Strategies for Using PBL:

  1. The Problem as a Guide - The problem is presented in order to gain attention prior to presenting the lesson.
  2. The Problem as an Integrator or Test - The problem is presented after readings are completed and/or discussed -- these are used to check for understanding.
  3. The Problem as an Example - The problem is integrated into the material in order to illustrate a particular principle, concept or procedure.
  4. The Problem as a Vehicle for Process - The problem is used to promote critical thinking whereby the analysis of how to solve it becomes a lesson in itself.
  5. The Problem as a Stimulus for Authentic Activity - The problem is used to develop skills necessary to solve it and other problems -- skills can include physical skills, recall of prior knowledge, and metacognitive skills related to the problem solving process. A form of authentic assessment of the skills and activity necessary in the content domain (Duffy & Cunningham, 1996, p.190).

 

 

 

 

REALs

Rich Environments for Active Learning. Based on constructivist ideas, REALs involve students in constantly shaping and reshaping knowledge constructed through their learning experiences. REALs may be implemented through cooperative learning, generative learning, student centered learning, and problem based learning (Schott).

Grabinger and Dunlap (1995) used the term to summarize the literature on constructivist learning theory and its five instructional design implications. Learning is active knowledge construction by learners, learners gaining knowledge in realistic contexts and the social negotiation of learning. Thus, learning environments should be characterized by five themes (Bostock, 1998, par. 14):

  1. Student responsibility and initiative
  2. Generative learning strategies
  3. Authentic learning contexts
  4. Authentic assessment
  5. Cooperative support

Bostock, S. (1998). Constructivism in mass higher education: A case study. Learning Technology. Retrieved August 24, 2002, from Keele University, Learning Technology Web site: http://www.keele.ac.uk/depts/cs/Stephen_Bostock/docs/sin98pa6.htm

Grabinger, S.R. and Dunlap, J.C. (1995) Rich environments for active learning: a definition. ALT-J, Journal of the Association for Learning Technology, 3 (2) 5-34.

Schott, M. (1999). Rich environments for active learning. Retrieved August 24, 2002, from San Diego State University, Encyclopedia of Educational Technology Web site: http://coe.sdsu.edu/eet/Articles/reals/start.htm

 

 

 

 

Reciprocal Teaching

Palincsar (1986) describes the concept of reciprocal teaching: Reciprocal teaching refers to an instructional activity that takes place in the form of a dialogue between teachers and students regarding segments of text. The dialogue is structured by the use of four strategies: summarizing, question generating, clarifying, and predicting. The teacher and students take turns assuming the role of teacher in leading this dialogue. Purpose: The purpose of reciprocal teaching is to facilitate a group effort between teacher and students as well as among students in the task of bringing meaning to the text.

Reciprocal teaching. (2002). Retrieved September 10, 2002, from North Central Regional Educational Laboratory, Pathways to School Improvement Web site: http://www.ncrel.org/sdrs/areas/issues/students/atrisk/at6lk38.htm

 

 

 

 

Situated Learning

Lave argues that learning as it normally occurs is a function of the activity, context and culture in which it occurs (i.e., it is situated). This contrasts with most classroom learning activities which involve knowledge which is abstract and out of context. Social interaction is a critical component of situated learning -- learners become involved in a "community of practice" which embodies certain beliefs and behaviors to be acquired. As the beginner or newcomer moves from the periphery of this community to its center, they become more active and engaged within the culture and hence assume the role of expert or old-timer. Furthermore, situated learning is usually unintentional rather than deliberate. These ideas are what Lave & Wenger (1991) call the process of "legitimate peripheral participation."

Other researchers have further developed the theory of situated learning. Brown, Collins & Duguid (1989) emphasize the idea of cognitive apprenticeship: "Cognitive apprenticeship supports learning in a domain by enabling students to acquire, develop and use cognitive tools in authentic domain activity. Learning, both outside and inside school, advances through collaborative social interaction and the social construction of knowledge." Brown et al. also emphasize the need for a new epistemology for learning -- one that emphasizes active perception over concepts and representation. Suchman (1988) explores the situated learning framework in the context of artificial intelligence.

Situated learning has antecedents in the work of Gibson (theory of affordances) and Vygotsky (social learning). In addition, the theory of Schoenfeld on mathematical problem solving embodies some of the critical elements of situated learning framework.

Brown, J. S., Collins, A. & Duguid, S. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42.

Kearsley, G. (n.d.). Situated learning (J. Lave). Retrieved September 10, 2002, from Explorations in Learning & Instruction: The Theory Into Practice Database Web site: http://tip.psychology.org/lave.html

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate periperal participation. Cambridge, UK: Cambridge University Press.

Suchman, L. (1988). Plans and situated actions: The problem of human/machine communication. Cambridge, UK: Cambridge University Press.

 

 

 

WebQuest(s)

A WebQuest is "an inquiry-oriented activity in which some or all of the information that students interact with comes from resources on the Internet" (Goldstein, 1997, para. 1). There are two types of WebQuests:

Short term WebQuest - Lasts one to three periods or days and its goal is basic knowledge acquisition. A good short term WebQuest will also include some type of subject integration.

Long term WebQuest - Takes between one week and on month to complete. A well planned long term WebQuest involves "extending and refining knowledge" (Goldstein, 1997, Types of WebQuests).

Goldstein, B. (1997). So - What's WebQuest???. Retrieved September 10, 2002, from http://webeducator.net/lab37/webquest/wqfaq.html#definition