Tuesday, December 10, 2013

What I've Learned about the field of Instructional Design & Technology



This semester, while teaching for the first time and taking a course about the epistemological, psychological, and educational foundations of the field of instructional design and technology, along with a course in instructional design and a course in cognitive psychology, I did a lot of generative processing (i.e. integrating and organizing of information from multiple sources about multiple things) about design of learning experiences.

In my initial post at the beginning of the semester about what constitutes good instructional design, I wrote that good instructional design is strategic design of learning experiences—based on good, internally coherent theory and unambiguous evidence—that can help develop learners develop knowledge and skills. I also wrote that good design of learning experiences is appropriate for learning objectives, entails a blend of learner analysis and evidence-based practices, and is conducive to stimulation of active learning and motivation. Most importantly, I wrote that quality design of learning experiences is not essentially technology-centric, but can leverage what technologies offer to enhance learning experiences in learner-centric and objective-centric ways.

I still agree with everything I originally wrote at the beginning of the semester—about both quality instructional design and the field of instructional design—and I think that my own learning experiences have only built on top of my personal knowledge and beliefs as well as my own instructional design skills. I think that the changes I have experienced in my knowledge and in my own instructional design skills can be classified according to following kinds of changes:
  1. Changes in my perspective of the field of instructional design
  2. Changes in my views on the importance of both multimedia and learner preferences to learning
  3. Changes in my views about motivation

The field of Learning Technologies a.k.a. IDT

I still stand by the idea that our field—which has been called many different names, such as the more recent names, Learning Technologies or Instructional Design & Technology—could have the most “justice done” to its name if it were called Learning Experience Design or LX design for short. The essence of the topic of every practice (research or instructional) of our field is learning. We are a learning-centric field—learning is built into what we do in our field in the sense that we must continue to learn to be good at what we do. More importantly, learning is the object of our obsessions and practices; it’s what we aim to facilitate and the benefits to others that we strive to offer in the ways in which we try to offer it.

I have learned this semester—particularly from the course about the foundations of our field—that research is incredibly important to our field, more so than I originally thought. Initially, I thought that there were only three ways in which research is relevant to what we do: (1) quality design of learning experiences requires “research” in the sense that it involves learner analysis and evaluation of learners’ demonstrations of their learning; (2) our field is built on the foundations that research and evidence-based theories in cognitive psychology and educational psychology have handed to us, and we add to that body of research-based knowledge through our own research about using instructional design and technology together to enhance learning; and (3) the instructional practices of our field are to accord with what research in our field and educational psychology have yielded. I think this is already a very research-centric conception of our field, but I have learned that there are at least two more ways that research is relevant to our field: (4) the research we conduct in academic departments about the use of instructional design techniques and technologies informs the academic research communities en masse about topics specific to our field (i.e. use instructional design and use of technology), but it also, in many cases, has the potential to inform many specialized bodies of interdisciplinary research about topics that are beyond mere use of technology or design (e.g. about science education, cognitive science, and foreign language education); and (5) research methods and models can be and often are applied to the design of learning experiences in workplaces to better understand and improve the effectiveness of instruction, not simply to evaluate or assess what learners have learned. For example, even in Kirkpatrick’s model of the four levels of evaluation of instruction-facilitated learning, research methods are vital to collection of data about learners and instructional methods.

In a nutshell, I think that our field has moved from its technology-centric origins at the beginning of the 20th century towards a more variegated scene of research-centric, learner-centric, and objective-centric activities that aim to improve learners. IDT activities, through the historical evolution of our field, have been influenced by epistemological theories about knowledge, cognitive science views on how people learn, socio-cultural views on the relevance of social and cultural factors to how people learn and to what learning objectives should be set, and models of intelligence that implicitly marry socio-cultural and cognitive science constructs in order to help guide how we analyze learners and the mechanics of learning. I think that our field now has the potential to contribute new kinds of evidence to academic disciplines that try to understand the mechanics of improvement of cognitive abilities (I kind of hope our field does do this), and I believe that it is the unique nature of our field that we uniquely attempt to translate these kinds of evidence into improvements in the ways that we design learning experiences and facilitate both acquisition of knowledge and skills. To clarify: This semester I wrote a mid-term and final paper about the relevance of cognitive (working memory) training interventions to how we prepare learners to be better novel problem solvers. I think our field is perhaps the best at emphasis on quality task analysis, and I think that is what is needed to best handle past and present research about cognitive training, brain training, and the use of interactive multimedia (e.g. action video games or serious games designed to target cognitive benefits) to improve human cognitive abilities. 

Multimedia and Learner Preferences

For my instructional design class, I had to write 2 reviews of recent research articles on an emerging instructional design topic. I chose adaptivity of hypermedia learning as my topic for which I would find two relevant articles. I chose this topic because I know from past experience and coursework that adaptive hypermedia would essentially involve the demand for using multimedia learning principles to design learning materials, and I was intrigued by the idea of changes in the multimedia design itself—not just changes in the way multimedia content is sequenced—as part of responding to learner performance.

Prior to this semester, I thought that accordance of the design of learning content to multimedia learning principles is essential to good instructional design, and I liked the idea of adaptive hypermedia systems that respond adaptively to a learner based on his or her performances, but not necessarily in ways that change the multimedia design itself. I had also thought about the potential importance of learner interests and preferences to learning, and I had done some of my own digging around in research databases to figure out that a learning style or cognitive style can be disambiguated to mean either (i) a preference or (ii) a tendency. However, I had not really thought much about the prospects of offering learners a choice between one kind of hypermedia design over another based on their preferences about how they like to learn. I think I has assumed that learner preferences can stray from what will actually be more conducive to helping them learn, and that this attitude towards learner preferences was built into the science of multimedia learning principles.

So, I chose one article that researched the effectiveness of giving learners a particular design of video content that aligned with their learning styles (preference of learning material type). I also chose another article about the fit between a learner’s choice between variations of learning materials (static, not dynamic like videos) that could satisfy their preferred style of learning and conduciveness to learning. After reviewing both articles, I interpreted their findings as support for my personal view: accordance of a design of multimedia learning materials to multimedia learning principles is more important and conducive to learning than is what a learner prefers or thinks will be the best kind of material.

Furthermore, the change I experienced in my personal views pertained to my views of the importance of metacognition and self-regulated learning to learning in hypermedia environments. I think that there are some important adaptive functions (e.g. adapting to learners’ prior knowledge and in-course achievements) that adaptive hypermedia can and should perform. However, I think that neither allowing learners to choose materials based on their self-concepts and preferences, nor designing multiple designs of learning materials to accord with differences in preferences is an effective instructional method; adaptive hypermedia should abandon this function, which is really a job that should be satisfied by appropriate application of multimedia learning principles to the facilitation of the learning task. Where the affordances of well-designed multimedia leave off, metacognition and self-regulated learning should take over to help better facilitate success in the learning task. Yes, each learner has individual differences in learning abilities, prior knowledge, and even in the very experience they have when “consuming” multimedia learning materials. Yes, sometimes these learner experiences, no matter how well a particular multimedia design is streamlined to facilitate learning and minimize cognitive load, can still include cognitive overload. However, the only thing that can best understand and overcome cognitive overload is the learner experiencing it, and they will overcome cognitive overload via self-regulated learning processes that inherently involve a metacognitive task. Metacognitive monitoring and metacognitive tasks cannot be offloaded onto a technology or tool outside of the particular learner, but we instructors can try to design experiences that promote development of metacognitive skills for overcoming the cognitive overload one experiences.  

Motivation

I know that learner motivation is crucially important to learning. I have known from readings and assignments from past courses (e.g. Learning to Learn & The Psychology of Learning) as well as from personal experiences that self-regulation and motivation are important to learning and, generally speaking, human performance.

Unfortunately I have also had to learn this in a more distinct way from another kind of personal experience. I have worked in some workplaces that are not always conducive to professional development or motivation of its employees. For example, when I took the Learning to Learn course and Computer Literacy Skills course together one summer, I was working at a Blackberry retail store where I experienced some corruption in store management. For example, store managers were giving my commission for phones I sold, to other employees who had not sold those phones, yet were friends with the store manager. Even worse, store managers and select employees were looking through people’s private data (pictures, videos, texts, browsing histories) on these phones that they were supposed to be merely charging for people. At this point I should clarify that this store was not tied directly to Blackberry (RIM); it was a retail store run by a retail management company. The main store manager was the husband of the daughter of the owner of the retail management company that ran the store…I quit after a month! It was really hard to be motivated to work in that kind of environment, and, the problem—the store manager—likely would not have been fixed. Plus, let’s be honest, sales in Blackberry smart phones were declining around that time anyway.

Another experience at the Blackberry store was just as unpleasant: their online trainings about the features of the different kinds of Blackberry phones. I had already decided to go into this field prior to working at the Blackberry retail store and was trying to gain entry to a master’s program in IDT, but this experience with the trainings there made me want to study and practice instructional design even more. The online trainings weren’t videos. They should be characterized as a form of hypermedia that featured hyperlinks for navigation, text, and images. This kind of hypermedia can be just as effective as an online video, if not more effective in some cases; the lack of video content was not the issue. The issue was that the text and images occupied the same positions. That’s right, the text was on top of the images such that only half of the text was readable. How can someone learn about a phone if the image of the phone and over half of the text about it are layered on top of each other? Basic multimedia learning principle here: they can’t. 

You know, our field is essentially about improving learners, and I identify with this professional identity, but I am having to learn that others may be in this field for others reasons, and that they may not always care about what really matters for quality instructional design or professional development. This realization is probably a good one to have for people in this field who do identify with the goals of quality instructional design, but the realization in itself can be a motivation “squasher.” I think that individuals who share the same professional identities—in terms of identifying with the goals of quality instructional design—should gravitate towards each other and stick together. They are probably more likely to get along with one another, motivate each other, and professionally develop together.

Whereas I used to think that, from the learner point of view, supporting motivation was primarily the job of self-regulation (when worse comes to worst, one should always try to be the kind of person who can count on him or herself for motivation), I now recognize that there are factors that really can make a difference in a learner’s experiences and enhance learning and human performance in ways that self-regulation alone cannot. These factors include self-efficacy, self-confidence, self-concept (e.g. professional identity), and things external to oneself (e.g. recognition of achievements, immediate feedback about one’s performance, or constructive feedback in general) that can help facilitate the presence of the aforementioned factors that are internal to oneself. I was learning this much from the learning theories I have encountered, but I think some of the workplace experiences I have had have helped me understand the importance of these motivational factors in ways that concrete experiences uniquely can. I can only imagine that there are some people who have had much worse experiences in workplaces, but have not been able to learn about motivational factors in the same ways that I have (i.e. from courses). When one knows about motivational factors, one has a larger, sharper knowledge base in long-term memory that can be used in moments of self-regulation.


Lastly, I am intrigued by the concept of flow—in game-based learning, in learning and human performance in general, and in workplaces. While I have only scratched the surface of this concept by light reading of brief articulations of the concept and theory behind it, I look forward to looking into some applications and operationalizations of the construct in research about the relationships between flow and learning.

Gee and Mayer on the Science of Learning, Problem Solving, and Games

Gee and Mayer on the Science of Learning, Problem Solving, and Games

In What Games Have toTeach Us About Learning and Literacy, literacy scholar James Paul Gee writes:

“What we are really looking for is this: the theory of human learning built into good video games. This theory is built into not just the games but also gamers and the gaming community. Of course, there is an academic field of cognitive science (or, better put, a part of it sometimes called “the learning sciences”). So we can, then, compare the theory of learning in good video games to theories of learning in cognitive science. Who’s got the best theory? Well, it turns out that the theory of learning in good video games is close to what I believe are the best theories of learning in cognitive science. And this is not because game designers read academic texts on learning” (Gee, 2007, p. 4).

In his chapter, “Multimedia Learning and Games,” from Tobias’ and Fletcher’s anthology, Computer Games and Instruction, cognitive scientist Richard Mayer writes:

“Many strong claims are made for the educational value of computer games, but there is little strong empirical evidence to back up those claims […] In order to provide guidance to game developers, it would be useful to have research-based principles for how to design educational computer games (i.e., a science of instruction) and a research-based theory of how people learn from educational computer games (i.e., a science of learning)” (Mayer, 2011, p. 281-2).

In one corner, we have Gee telling us that using a semiotics-based framework to reflect on playing games, to study the ways games are designed, and to study games, can help us not only understand the learning that happens in existing games (e.g. off-the-shelf commercial games like World of Warcraft) so that we can design educational games, but also principles of learning, motivation, and assessment that can help us remodel formal education in school systems towards a system of in-class and out-of-class learning experiences that fit “better with the modern, high-tech, global world today’s children and teenagers live in than do the theories (and practices) of learning that they sometimes see in school” (Gee, 2007, p. 5).

In the other corner, we have Mayer telling us that we still need to build an empirical base of research-supported instructional design principles for designing educational games that effectively help students achieve learning objectives, and that the existing base of multimedia learning principles can help us reframe the science of game-based learning and educational game design practices towards a body of research-supported principles that can operate with existing instructional design principles (287-8).

Who is right, Gee or Mayer?

Well, right about what—game design, instructional design in general, or the fit between k-12 and higher education learning experiences with learners entering them? All three of these things can be addressed separately with respect to the usefulness or strengths of Gee and Mayer’s positions. Below I address Gee and Mayer’s positions on game design and instructional design in more depth, but first I quickly address their positions on the fit between k-12 and higher education learning experiences with the learners entering them.

Designing Education for Digital Natives

In “Digital Natives, DigitalImmigrants,” Marc Prensky writes that widespread access to and use of digital technologies in and since the 1990s (in the U.S.) by those born into this digital revolution has been an irreversible event that should be accounted for in the design of learning experiences in formal education (p. 1). Prensky coins the terms digital native and digital immigrant to emphasize this fundamental difference in students before and after the digital revolution in terms of they ways in which they learn and process information, not just in terms of their birthdays and technology use. In short, a digital native is a person who thinks and processes in certain ways from use of digital tools and toys, prioritize consumption of information from computers, prefer digital media over reading text, prefer fast delivery of information and immediate results or feedback, and, generally speaking, prefer shifting their attention to creation and sharing of digital content.

Gee seems to marry the need to design learning experiences that fit digital natives and digital immigrants to his semiotics-based framework.

To my knowledge, Mayer does not espouse a digital native-based theoretical approach to studying instruction, learning, problem solving, or games. Presumably, Mayer is interested in principles of instructional design and evidence about learning experiences that will hold true across people and scenarios regardless of whether or not the participants in the research studies are “digital natives,” “digital immigrants,” or just plain computer illiterate. I think I have seen at least one Mayer study mention the relevance of computer literacy to their research on games. If Mayer—or someone with the same interests and understanding of multimedia learning principles and the science of learning—has not already operationalized the concepts of digital native in relation to (interactive) multimedia and studied the impact of the potential advantages or disadvantages of digital native dispositions, I would like to see whether or not this kind of research study would result in evidence of a set or cluster of individual differences in relevant dispositions, characteristics or traits between digital natives and digital immigrants (or computer illiterate).

Designing Games with Learning Objectives

Imagine that you are to design a learning experience that facilitates development and transfer of a kind of problem solving (e.g. solving algebraic equations) for your learners, and you want a set of principles to help guide your design.

I worry that Gee’s last statement, “And this is not because game designers read academic texts on learning” borders on something that Ntiedo Etuk, founder of DimensionU, said about game designers and designing games for learning; see one of my previous posts about game-based learning for this. It’s as if both Etuk and Gee are suggesting that ordinary game designers (those who don’t read academic research on the learning sciences) use intuition or reflections on private experiences during gameplay to design the game, and then test the design of the game via playtesting. If Etuk and Gee are not saying this, then I would ask for more clarification about their positions, but I will continue reflecting on this intuition-reflective design-playtest model anyway.

‘Intuition’ could mean (a) the passive ability to acquire knowledge without use of any sort of reasoning abilities (deductive or inductive) or (b) recollection of prior knowledge of any kind. I am more likely to accept the relevance of the second sense of intuition to instructional design than I am the first, especially if intuition in the second sense is in no way polluted by intuition in the first sense. Intuition in the first sense is blind of both experience and prior knowledge; I don’t believe it exists, and if it did, it wouldn’t be useful. Insofar as intuition in the second sense does not hinge on intuition in the first sense, and people have active control over experience, inference, construction of meaning and knowledge, and recollection of prior knowledge (they do), it can be useful, but may not always be accurate if beliefs about others’ experiences (e.g. gameplay and learning) are only grounded in one’s personal experiences. If this intuition-reflective design-playtest model of game design processes is polluted by intuition in the first sense about how other people will experience or learn from gameplay, or it does not account for relevant differences beyond the designer’s personal experience, implementing this intuition-reflective design-playtest model of game design risks counterproductive design of game features. Plus, if playtesting procedures used do not accord with the virtues of academic experimentation, and the design of the game was meant to facilitate learning, then the playtesting and design together may sacrifice validity, reliability, and conduciveness to facilitating learning for those who end up playing the game.

Designing Instruction


Interestingly, there are some similarities in their learning principles. Aside from shared commonalities here, I agree with Mayer that instructional design of any kind should not only align formal or informal learning objectives with appropriate features of learning environments, but also be evidence-based. I also agree with Gee that we should view cognitive achievements during and as a result of gameplay with an eye for what is not just an individual achievement, but also a social or cultural achievement.

Tuesday, November 12, 2013

Gee’s 36 learning principles

Gee’s 36 learning principles

These principles are typed verbatim from the Appendix to What Games Have to Teach Us About Literacy and Learning. Throughout the book, Gee positions these as principles that good games have and that learning in and out of schools should have.

  1. Active, Critical Learning Principle: All aspects of the environment (including the ways in which the semiotic domain is designed and presented) are set up to encourage active and critical,  not passive, learning.
  2. Design Principle: Learning about and coming to appreciate design and design principles is core to the learning experience.
  3. Semiotic Principle: Learning about and coming to appreciate interrelations within and across multiple sign systems (images, words, actions, symbols, artifacts, etc.) as a complex system is core to the learning experience.
  4. Semiotic Domains Principle: Learning involves mastering, at some level, semiotic domains, and being able to participate, at some level, in the affinity group or groups connected to them.
  5. Metalevel thinking about semiotic domains principle: Learning involves active and critical thinking about the relationships of the semiotic domain being learned to other semiotic domains.
  6. “Psychosocial Moratorium” Principle: Learners can take risks in a space where real-world consequences are lowered.
  7. Committed learning principle: Learners participate in an extended engagement (lots of effort and practice) as an extension of their real-world identities in relation to a virtual identity to which they feel some commitment and a virtual world that they find compelling.
  8. Identity principle: Learning involves taking on and playing with identities in such a way that the learner has real choices (in developing the virtual identity) and ample opportunity to mediate on the relationship between new identities and old ones. There is a tripartite play of identities as learners relate, and reflect on, their multiple real-world identities, virtual identity, and a projective identity.
  9. Self-knowledge principle: The virtual world in constructed in such a way that learners learn not only about the domain but about themselves and their current and potential capacities.
  10. Amplification of input principle: For a little input, learners get a lot of output.
  11. Achievement principle: For learners of all levels of skill there are intrinsic rewards from the beginning, customized to each learner’s level, effort, and growing mastery and signaling the learner’s ongoing achievements.
  12. Practice principle: Learners get lots and lots of practice in a context where the practice is not boring (i.e., in a virtual world that is compelling to the learners on their own terms and where the learners experience ongoing success). They spend lots of time on task.
  13. Ongoing learning principle: The distinction between learner and master is vague, since learners, thanks to the operation of the “regime of competence” principle listed next, must, at higher and higher levels, undo their routinized mastery to adapt to new or changed conditions. There are cycles of new learning, automatization, and undoing automatization, and new reorganized automatization.
  14. “regime of competence” principle: The learner gets ample opportunity to operate within, but at the outer edge of, his or her resources, so that at those points things are felt as challenging but not “undoable.”
  15. Probing principle: Learning is a cycle of probing the world (doing something); reflecting in and on this action and, on this basis, forming a hypothesis; reprobing the world to test this hypothesis; and then accepting or rethinking the hypothesis.
  16. Multiple routes principle: There are multiple ways to make progress or move ahead. This allows learners to make choices, rely on their own strengths and styles of learning and problem solving, while also exploring alternative styles.
  17. Situated meaning principle: The meanings of signs (words, actions, objects, artifacts, symbols, texts, etc.) are situated in embodied experience. Meanings are not general or decontextualized. Whatever generality meanings come to have is discovered bottom up via embodies experiences.
  18. Text principle: Texts are not understood purely verbally (i.e. only in terms of the definitions of the words in the text and their text-internal relationships to each other) but are understood in terms of embodied experiences. Learners move back and forth between texts and embodied experiences. More purely verbal understanding (reading texts apart from embodied action) comes only when learners have had enough embodied experience in the domain and ample experiences with similar texts.
  19. Intertextual principle: The learner understands texts as a family (“genre”) of related texts and understands any one such text in relation to others in the family, but only after having achieved embodied understandings of some texts. Understanding a group of texts as a family (genre) of texts is a large part of what helps the learner make sense of such texts.
  20. Multimodal principle: Meaning and knowledge are built up through various modalities (images, texts, symbols, interactions, abstract design, sound, etc.), not just words.
  21. “material intelligence” principle: Thinking, problem solving, and knowledge are “stored” in tools, technologies, material objects, and the environment. This free learners to engage their minds with other things while combining the results of their own thinking with the knowledge stored in these tools, technologies, material objects, and the environment to achieve yet more powerful effects.
  22. Intuitive knowledge principle: Intuitive or tacit knowledge built up in repeated practice and experience, often in an association with an affinity group, counts a great deal and is honored. Not just verbal and conscious knowledge is rewarded.
  23. Subset principle: Learning even at its start takes place in a (simplified) subset of the real domain.
  24. Incremental principle: Learning situations are ordered in the early stages so that earlier cases lead to generalizations that are fruitful for later cases. When learners face more complex cases later, the hypothesis space (the number and type of guesses the learner can make) is constrained (guided) by the sorts of fruitful patterns or generalizations the learner has found earlier.
  25. Concentrated sample principle: The learner sees, especially early on, many more instances of fundamental signs and actions than would be the case n a less controlled sample. Fundamental signs and actions are concentrated in the early stages so that learners get to practice them often and learn them well.
  26. Bottom-up basic skills principle: Basic skills are note learned in isolation or out of context; rather, what counts as a basic skill is discovered bottom up by engaging in more and more of the genre/domain or game/domain like it. Basic skills are genre elements of a given type of game/domain.
  27. Explicit information on-demand and just-in-time principle: The learner is given explicit information both on demand and just in time, when the learner needs it or just at the point where the information can best be understood and used in practice.
  28. Discovery principle: Overt telling is kept to a well-thought-out minimum, allowing ample opportunity for the learner to experiment and make discoveries.
  29. Transfer principle: Learners are given ample opportunity to practice, and support for, transferring what they have learned earlier to later problems, including problems that require adapting and transforming that earlier learning.
  30. Cultural models about the world principle: Learning is set up in such a way that learners come to think consciously and reflectively about some of their cultural models regarding the world, without denigration of their identities, abilities, or social affiliations, and juxtapose them to new models that may conflict with or otherwise relate to them in various ways.
  31. Cultural models about learning principle: Learning is set up in such a way that learners come to think consciously and reflectively about their cultural models of learning and themselves as learners, without denigration of their identities, abilities, or social affiliations, and juxtapose them to new models of learning and themselves as learners.
  32. Cultural models about semiotic domains principle: Learning is set up in such a way that learners come to think consciously and reflectively about their cultural models about a particular semiotic domain they are learning, without denigration to their identities, abilities, or social affiliations, and juxtapose them to new models about this domain.
  33. Distributed principle: Meaning/knowledge is distributed across the learner, objects, tools, symbols, technologies, and the environment.
  34. Dispersed principle: Meaning/knowledge is dispersed in the sense that the learner shares it with others outside the domain/game, some of whom the learner may rarely or never see fact to face.
  35. Affinity group principle: Learners constitute an “affinity group,” that is, a group that is bonded primarily through shared endeavors, goals, and practices and not shared race, gender, nation, ethnicity, or culture.
  36. Insider principle:  The learner is an “insider,” “teacher,” and “producer” (not just a “consumer”) able to customize the learning experience and domain/game from the beginning and throughout the experience.