Across the History Curriculum. Part 9
“Knowing is not enough; we must apply. Willing is not enough; we must do.” (Johann Wolfgang von Goethe; German writer and polymath; 1749-1832.) [Bold added for emphasis.]
“Civilization advances by extending the number of important operations which we can perform without thinking of them.” (Alfred North Whitehead; English mathematician and philosopher who wrote on algebra, logic, foundations of mathematics, philosophy of science, physics, metaphysics, and education, and co-authored the epochal Principia Mathematica with Bertrand Russell; 1861-1947.)
“One striking fact is that the complex world of education—unlike defense, health care, or industrial production—does not rest on a strong research base. In no other field are personal experience and ideology so frequently relied on to make policy choices, and in no other field is the research base so inadequate and little used.” (National Research Council; 1999, link).
This IAE Newsletter expands my exploration of Computer Assisted Learning (CAL). The previous newsletter states that “the term CAL is used very broadly to include all forms of online teaching and learning, no matter where or for what purpose it occurs.”
CAL is designed to aid learners in gaining the knowledge and the skills that they can use to solve problems and accomplish tasks within the disciplines they are studying. I have been interested in problem solving throughput my career, and I have taught and written extensively about solving problems and accomplishing tasks (Moursund, 2011).
I use a very broad definition of problem solving. For example, a teacher faces the problem of helping students learn, while a student faces the problem of learning. A teacher faces problems of deciding what content students are to learn, what are effective ways to help students learn the content, and how to assess both what students are learning (formative evaluation) and what they have learned (summative evaluation). A teacher faces the problem that no two students are identical—indeed, there are huge differences between the students in each class. A student faces the problem that the teacher is teaching to the whole class, rather than specifically to the student.
The first of the three quotations given at the beginning of this newsletter stresses learning that supports doing. We want education to empower students. That is, we want students to gain knowledge and skills that will help them to solve the problems and accomplish the tasks that they are meeting at the current time in their lives, and help to prepare them to deal effectively with future problems and tasks they will encounter.
Humans are good at creating and using empowering tools. Think about rock knapping, to make sharp-edged stones. This was developed by prehumans more than 3 million years ago. Two types of learning are involved in this endeavor. First, there is learning to select appropriate rocks and how to “knap” them. Second, there is the challenge of learning to use the resulting sharp rocks—for example, to cut and scrape animal pelts, and to make use of the products so produced.
Notice that a pre-human could become skilled in either or both of these two tasks. The same idea holds for the tools that we humans use in our lives. I routinely use a car, a computer, a Smartphone, and a pair of hearing aids. Each empowers me—helps me to do certain things. I lack the knowledge and skills to make any of these tools.
In terms of mass public education, this train of thought leads to the questions: What problems and tasks do we want our students to learn about and to solve? What tools do we want them to learn to use, both as an aid to learning and as an aid to using what they learn?
These are challenging questions, and appropriate answers change as the capabilities and availability of relevant tools change. I am specifically interested in computers. What do we want students to learn how to do using computers? For example, do we want them to learn to use CAL as an aid to learning? Do we want them to learn to make use of computers to solve some of the problems and accomplish some of the tasks that they currently are learning to do without computers? Do we want them to learn to write computer programs?
The second quotation stresses gaining an education that builds on the previous work of others. This is sometimes stated as, “Don’t reinvent the wheel.” That is, build on the previous successful work of others. This is very profound advice. Think about its application to curriculum development. Consider a teacher newly graduated from college and now preparing lessons for the first day of classes. This newly minted teacher might consider creating entirely new content, teaching processes, and evaluation for a course. But, the course has been taught by many teachers over many years. The new teacher does not have the time nor the skills to quickly design new content, teaching methods, and assessment procedures.
Please reread the third quotation. For more than 60 years, people have been doing research on CAL, and they have made a lot of progress. A CAL-based collection of lessons or a course can be tried out with a collection of students. This can be a very large “sample” incorporating many students from diverse locations who are using the same instructional materials. The results can be analyzed, the materials can then be revised, tried out with a different collection of students, revised, and on and on and on. The CAL system that is interacting with the students can gather detailed information that is essential to the research effort. Through this research and development process, we can make significant progress in producing more effective and more cost-effective CAL materials. This is routinely happening now!
Teaching is a complex, challenging vocation. Andrew Johnson defines teaching as an art, a science, and a craft (Johnson, 11/10/2015, link):
It is an art in that teachers must bring themselves fully into their teaching. As a teacher you will need to find the methods and strategies that work best for you. Teachers are not standardized products. What works for one teacher may not work for another. To be an effective teacher you must carve out your own teaching philosophy and discover your own unique talents and learn how to use them.
It is a science in that there are strategies and practices that a body of research has shown to be effective in enhancing learning. Just like doctors, teachers should use research to inform their practice. On the individual level teaching is a science also in that teachers are constantly collecting data by observing [and testing] their students in order to see if learning is taking place and how they learn best. And, like scientists, teachers experiment with new techniques or strategies to see how they work.
Teaching might also be described as a craft. A craft is a skill or set of skills learned through experience. This is exactly what teaching is. This means that one cannot expect to leave a college teacher preparation program as a finished teaching product. Teaching is a complex, multi-dimensional endeavor; not something that can be mastered in four semesters. Master teachers develop over time through experience and continued study and reflection. [Bold added for emphasis.]
As described above, teaching is a human endeavor. Let us reexamine each of the bolded statements in the quote from Johnson.
- Teachers are not standardized products. Each teacher has unique capabilities and limitations related to their ability to adjust to fit the needs of individual students. One of the potential strengths of CAL is that it can adjust to the individual student. However, it is a major research challenge to develop CAL that understands a student well enough to adjust appropriately to the learning needs of that student. We have a long way to go in this endeavor.
- …teachers are constantly collecting data by observing [and testing] their students… In a number of aspects of testing, CAL systems can do real time formative evaluation and provide immediate feedback. However, so far computers are not good at reading a student’s face, posture, interactions with other students, and so on, and then making appropriate decisions based on the information garnered in this manner. Also, they are not good at understanding a student’s oral or written response (“show your work”). Computers have a very long way to go before they can begin to approach the skills of a human teacher in such endeavors.
- A craft is a skill or set of skills learned through experience. I have heard from many seasoned teachers that it takes new teachers about a half-dozen years to gain a reasonably good level of skills. Some of these craft skills can be built into CAL systems.
My belief is that, at the current time, a good human tutor who is working one-on-one with a student is far better that the best of current CAL systems. But, when we expect the teacher to handle a class of 20, 30, or even more students, the story changes. In this setting at the current time, an appropriate combination of CAL and human teacher is very likely to be more effective than either one alone.
I have no doubts that CAL will become better and better over time. Thus, our educational systems face a growing problem of how to make appropriate use of CAL. Our current teacher preservice and inservice education programs leave much to be desired in helping teachers learn to deal with CAL. Another problem is that many of our current teachers did not gain their own precollege education in an environment that made routine use of CAL. A third problem is that, as more and better CAL is routinely being developed, teachers will need continuing inservice training to implement the new programs effectively.
Consider the difficulties a teacher faces when a new textbook series is adopted, typically every six or seven years. Contrast this with CAL materials that are being revised and updated on a much shorter time schedule—perhaps with significant updates coming out several times year. Add to this the challenge of dealing with the problems that students have as they struggle with the hardware and software problems inherent in learning any new technology. (And some of the students will likely have much more general knowledge about computers than their teachers.)
A human brain has marvelous capabilities. Your brain is receiving input through a number of sensors, processing this input, and acting upon the new information 24 hours a day. That is, your brain is learning and then making use of its previous and current learning all the time, both when you are awake and when you are asleep.
Feedback is an essential component of learning. While you are learning, you can provide feedback to yourself via metacognition (Chick, n.d., link):
Metacognition is, put simply, thinking about one’s thinking. More precisely, it refers to the processes used to plan, monitor, and assess one’s understanding and performance. Metacognition includes a critical awareness of a) one’s thinking and learning and b) oneself as a thinker and learner.
More than twenty years ago, I asked one of my Early Childhood colleagues in the University of Oregon College of Education about metacognition. She indicated that she could teach metacognition to four-year old children. More recent research supports this position (Eccles, July, 2015, link).
Many researchers have studied learning. My 4/19/2020 Google search of the expression learning theory produced 832 million results. Researchers have developed both general theories of learning and ways to help people learn more, better, and faster. Six of these theories are discussed in the article, Six Learning Theories that Every Educator Must Know (Graycaps, n.d., link):
- Multiple Intelligences Theory
- Bloom’s Taxonomy
- Zone of Proximal Development (ZPD) and Scaffolding
- Schema and Constructivism
- Spiral Curriculum
Perhaps you are not familiar with one or more of these. You can remedy this situation by use of the Web—a type of self-inservice.
My purpose in providing this list is to help you to realize that teaching and learning are complex activities. In a CAL development project, the team members are apt to have knowledge about a broad range of learning theories and instructional methodologies. It is a huge challenge to design CAL materials that are well grounded in the current research on teaching and learning. Currently available CAL materials vary widely in the extent to which they are solidly supported by the research in theories of teaching and learning.
In addition, the computer system being used to deliver CAL can also solve a wide range of problems and accomplish a wide range of tasks within the specific discipline area being taught. This raises the exceedingly important question: What do we want students to learn via their CAL use? Stated simply, do we want them to learn to solve the problems and accomplish the tasks in a computer-aided environment, in a by-hand environment, or in some combination of the two?
This same type pf question has been with us since prehumans first began to develop tools that they used to solve problems and accomplish tasks. How does one handle the teaching/learning situation when a new tool is developed that will be a better aid? Repeating the quote from Alfred North Whitehead at the beginning of this newsletter, “Civilization advances by extending the number of important operations which we can perform without thinking of them.”
Whitehead was a very noted English mathematician and philosopher, and his statement made more than a hundred years ago holds true for today. It is applicable in all areas of problem solving, but is perhaps best illustrated in mathematics. We humans now have several thousand years of accumulated mathematical results that are still valid. When faced by a math problem, a good starting point is to determine whether it has already been solved. If it has not been solved, then a good approach is to try to break the new problem into a collection of smaller problems, and check to see which of these have already been solved. If they have all been previously solved, then it is straightforward to solve the new problem. If one or more of the smaller problems have not been solved, than one has reduced the original problem to one of wanting to solve the unsolved smaller problem(s).
Actually, my use of the term straightforward is incorrect. It is one thing to know that a particular problem has been solved, and it is an entirely different thing to know how to solve it. But, suppose that computers and computerized machines can solve each of the individual smaller problems. Then, indeed, the original problem is solved by making use of computers and computerized machines to solve the smaller problems—even if one does not know how to solve each of them using one’s own brain and by-hand methods (Moursund, 2011).
This analysis applies to learning to solve problems and accomplish tasks in any discipline of study. The steadily improving capability of computers as an aid to problem solving means that educators need to rethink the content, pedagogy, and assessment in all teaching areas.
In December, 1980, my good friend Robert Taylor published The Computer in the School: Tutor, Tool, Tutee (Taylor, 1980). The book contains a collection of previously published articles written by a variety of authors, and I had the pleasure of writing the Preface. The title refers to three general uses of computers that were well established by that time in business and industry as well as in the schools. I have added toy as a fourth item to the list (see the first paragraph after this list).
- Tutor. The computer serves as a type of teacher, interacting with a student to present information to be learned and also to assess student learning.
- Tool. Computers and computerized machines provide a very wide range of tools that can help to solve problems and accomplish tasks.
- Tutee. This refers to students writing computer programs. Computer programming was a well-developed discipline of study by the time Taylor wrote his book, with programing languages such as FORTRAN (1957), COBOL (1959), BASIC (1963), and Logo (1967) being taught in many schools.
- Toy (Educational Computer Game). My 4/18/2020 Google search for the expression Educational Computer Games produced about 537 million results. My search for the expression Free Educational Computer Games produced about 355 million results. Who says, “Learning should not be fun”? Just think about the fun of flying a simulated jet airplane or rocket ship, where the simulations are so good that they are used to train pilots and astronauts.
In a private conversation, Taylor told me that he had thought about adding Toy as a fourth category of computer uses. I believe he decided against this because it detracted from the “scholarly, academic” nature of the book. The first video games were developed in the early 1950s, and computer gaming was well established by 1980. Some parents and teachers feared that if students were allowed to access computers while in school, they would “waste” a lot of time playing games. Now, 40 years after the publication of Taylor’s book, this still remains a major concern.
My 5/8/2020 Google search of the expression research-based free world’s history simulation game produced about 72 million results. As one example, high school history teacher Jeremiah McCall provides an interesting insight into the value of historical simulation games (McCall, 2011, link):
Historical simulation games have the power to immerse students in a world of conflicting goals and choices where they have the power to make decisions and experience (virtually) the consequences of those decisions. When playing a simulation, as opposed to reading a text, listening to a lecture, engaging in a discussion, or watching a film, the learner can, ideally, confront firsthand the constraints human actors in the past faced. They can learn about the scarcity of resources, importance of systems, ties of relationships, and a host of other things. Simulations can encourage learners to consider the historical and physical contexts people in the past faced and, best of all, to view the past as the result of myriad human choices that were not preordained in any sense.
Compared to the thousands of years we have had reading and writing, as well as the schools to teach these subjects, CAL has a relatively short history. This section mentions just a few highlights in that history.
In 1924-1925, long before the development of the first electronic digital computer, Sydney Pressey, Professor of Psychology at Ohio State University, developed a teaching machine (Benjamin, 1988, link). The machine was designed for administering multiple-choice questions to students. He called it an adjunct auto instruction. The machine had a window that displayed a question and four answers on paper. The student pressed the key to choose an answer. The machine recorded a student’s answer on a counter and showed the next question. Over many years, he improved his machine and studied the effects of its use.
Pressey was not impressed by the effectiveness of his teaching machine as an aid to learning, and spent many years working to improve it. He eventually decided that just having students read on their own was a more effective means of education.
Mentally, I compare Pressey’s machine to a multiple-choice type of flashcards. Paper flashcards have a long history of successful use as an aid to memorizing facts. However, a good education (good learning) is far more than rote memorization of facts.
Still, there are a number of Web sites that provide free software for creating and using computerized flashcards. My 4/22/2020 Google search of the expression free flashcard programs produced about 745,000 results.
Patrick Suppes, who died in 2014 at the age of 92, was one of the pioneers in the field of CAL (Friedman, 11/25/2014, link):
In 1990 Suppes was awarded the National Medal of Science by President George H. W. Bush in recognition of “his broad efforts to deepen the theoretical and empirical understanding of four major areas: the measurement of subjective probability and utility in uncertain situations, the development and testing of general learning theory, the semantics and syntax of natural language and the use of interactive computer programs for instruction.” [Bold added for emphasis.]
I am impressed by Suppes’ remarkable insights into CAL. The following quotation from an article he wrote for The Encyclopedia of Educational Media Communications and Technology summarizes some of his thinking (Suppes, 1988, link):
Before discussing the substantive developments in CAI, [now more commonly called CAL] there is one general issue that is worth elaboration. It is the question of whether or not computers and related forms of high technology constitute a new restraint on individuality and human freedom. There are several points I would like to make about the possible restraints that widespread use of computer technology might impose on education. The … history of education is a history of the introduction of new technologies, which at each stage have been the subject of criticism. Already in Plato’s dialogue Phaedrus, [about 360 BCE] the use of written records rather than oral methods of instruction was criticized by Socrates and the Sophists. The introduction of books marked a departure from the personalized methods of recitation that were widespread and important for hundreds of years until this century.
Mass schooling is perhaps the most important technological change in education in the last one hundred years. It is too easy to forget that as late as 1870 only 2 percent of the high school-age population in the United States completed high school. A large proportion of the society was illiterate; in most other parts of the world the population was even less educated. Moreover, the absence of mass schooling in many parts of the world as late as 1950 is a well-documented fact. The efforts to provide mass schooling and the uniformity of that schooling in its basic structure throughout the world are among the most striking social facts of the twentieth century.
My second point is that the increasing use of computer technology can provide a new level of uniformity and standardization. Many features of such standardization are of course to be regarded as positive insofar as the level of instruction is raised. There are also opportunities for individualization of instruction that will be discussed more thoroughly in later sections, but my real point is that the new technology does not constitute in any serious sense a new or formidable threat to human individuality and freedom. [Bold added for emphasis.]
When I read the second point, words such as misinformation and indoctrination jumped into my head. My 5/5/2020 Google search of the expression brainwashing and indoctrination in education produced about 553,000 results.
Suppes takes the position that CAL does not add to this threat of loss of human individuality and freedom. I find this to be an interesting assertion. The increased uniformity, standardization, and openness provided by CAL make the curriculum content and teaching process openly visible to anyone who wants to examine the CAL materials. This can be thought of as an extension of examining (and possibly attempting to ban) the books that students are being required or allowed to read. Contrast this openness of CAL materials with attempting to identify the actual content being taught by an individual teacher.
Art Luehrmann is best known for the large number of computers in education books he wrote, and for a 1972 article, Should the Computer Teach the Student, or Vice Versa, in which he introduced and defined the term computing literacy (Luehrmann, 2002, link). The term used more generally today is computer literacy. The article was included in Robert Taylor’s 1980 book, The Computer in the School: Tutor, Tool, Tutee, mentioned earlier in this newsletter.
The focus of Leuhrmann’s 1972 article was the issue of schools teaching students to write their own computer programs versus schools only teaching students to make use of computer programs (tools) written by other people. He argued that teaching students how to program at a beginner’s level was an essential component of a good education.
Schools around the world vary considerably in their emphasis on teaching computer programming to all students. A major difficulty is that most teachers do not know how to program. Thus, schools face the dual challenge of who will teach computer programming and how the non-programming teachers will deal with their students who are skilled in computer programming.
We know that young students can learn to write computer programs. My personal experience suggests that students who have both an interest in as well as a knack for learning to write computer programs at a young age will be able to achieve a high level of success. This success can make a major contribution to their lives, both while still in school and in their future careers and avocations.
Seymour Papert was a world leader in the early promotion of the use of computers in school, and one of the developers of the Logo programming language (Wikipedia, 2020a, link). Quoting from his book, Mindstorms: Children, Computers, and Powerful Ideas (now available as a free PDF file) (Papert, 2001, link):
In many schools today, the phrase computer-aided instruction means making the computer teach the child. One might say the computer is being used to program the child. In my vision, the child programs the computer and, in doing so, both acquires a sense of mastery over a piece of the most modern and powerful technology and establishes an intimate contact with some of the deepest ideas from science, from mathematics, and from the art of intellectual model building. [Bold added for emphasis.]
Nowadays, via a combination of informal and formal education, most students in the world’s economically developed countries learn to use a variety of computer tools, e.g., a word processor, a search engine for searching the Web to access text and audio-video content, social networking tools and/or email, taking still and motion pictures on a smartphone, and so on.
The Programming Language Named Scratch
Both Luehrmann and Papert strongly supported the idea of young children learning to write programs. While Logo still is available free and is widely used, a newer free language named Scratch was first made widely available in 2003. Scratch now has a large number of users and a solid educational support structure (Scratch, n.d., link):
Scratch is designed especially for ages 8 to 16, but is used by people of all ages. Millions of people are creating Scratch projects in a wide variety of settings, including homes, schools, museums, libraries, and community centers.… Scratch is used in more than 150 different countries and available in more than 40 languages.
A human tutor working with a student draws on a very broad range of intelligences, knowledge, and skills. No current CAL system comes close to having such a broad range of capabilities. However, educational research and progress in artificial intelligence (AI) are certainly improving the capabilities of CAL.
I have long been interested in CAL and AI. Many years ago, I coined the phrase, Highly Interactive Intelligent Computer Assisted Learning (HIICAL) (Moursund, September, 2002, link). The phrase HIICAL emphasizes the importance of student interaction with an intelligent agent—the essence of tutoring by a human.
Intelligent Tutoring System (ITS)
The term Intelligent Tutoring System (ITS) now has come into common use (rather than my term HIICAL). Some of the progress and challenges in ITS are summarized in an interview with professor Arthur Graesser (Anderson, 11/26/2018, link):
At the University of Memphis, Dr. Graesser is developing intelligent tutoring systems (ITS), such as AutoTutor, a virtual tutor that helps students comprehend difficult concepts and manage their emotions as they tackle them. EdSurge spoke with him about ITS and how it encourages students to go beyond memorization and practice the concepts they’re learning. He highlighted crucial aspects of deep learning—why you don’t usually find it in traditional classrooms, how conflict and confusion can inspire it, why people don’t like it, and why it’s so important for today’s students to achieve it.
Question: Intelligent tutoring systems are computer systems that simulate human tutors by providing customized instruction and feedback to learners. What drove you to develop these systems? What was the problem you were trying to solve?
Art Graesser: A lot of the learning that goes on is shallow learning: memorizing things and being exposed to ideas, for example. But to put those concepts into practice, you need deeper learning. We have evidence that when you take a demanding test that requires reasoning, reading a book or listening to a lecture in preparation is no different than doing nothing. It’s not until you have an interactive learning environment that you can get to the deeper learning. That’s where intelligent tutoring systems come into play. [Bold added for emphasis.]
Question: What are some of the challenges with deeper learning?
Art Graesser: People don’t like it! Thinking hurts! When you get ratings from classes, students tend to like the easier classes with less challenging material. That’s where it’s important to track the emotions of the learner. We’ve identified what the learning-centered emotions are. With advances in educational data mining, we can infer learners’ emotions from their natural language interaction with systems, their facial expressions, even their body posture. So, if a learner is frustrated or disengaged, you have to do something adaptively. One of my favorite emotions is confusion. Confusion predicts whether or not people are thinking. [Bold added for emphasis.]
During the past two decades, substantial progress has occurred in ITS. However, we still have a very long way to go before it can even begin to match a good human tutor who is backed up with today’s interactive multimedia and other aids to teaching and learning.
Massive Open Online Courses (MOOCs)
A massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the web. In addition to traditional course materials, such as filmed lectures, readings, and problem sets, many MOOCs provide interactive courses with user forums or social media discussions to support community interactions among students, professors, and teaching assistants (TAs), as well as immediate feedback to quick quizzes and assignments. MOOCs are a recent and widely researched development in distance education, first introduced in 2008 and emerged as a popular mode of learning in 2012 (Wikipedia, 2020b, link).
While most MOOCs are designed for college undergraduate or graduate students, quite a few of these are suitable for precollege students. Other MOOCs are being designed specifically for precollege students. A number of teacher education MOOCs also are available (MOOC List, 2020, link; My MOOCS, 2020, link).
Many MOOC courses can be taken at no cost. However, there typically is a cost if one wants graded assignments, college credit, and/or a certificate of completion.
Although thousands of MOOCs have been developed, and many tens of millions of people have enrolled in these courses, the overall MOOC movement has not fulfilled its original expectations. For example, less than 15 percent of people who sign up for a course actually complete the course (Lederman, 1/16/2019, link; Lederman, 2/14/2018, link).
It is obvious to me that human teachers are better than CAL in a very wide range of teaching and learning situations. However, it also is clear to me that CAL is better than human teachers in an increasing number of situations. Thus, schools are faced by the challenge of appropriately combining the two methodologies in a learning-effective and cost-effective manner to fit the varying needs of the wide range of students in our schools.
We have thousands of years of experience and research leading to improvements in the performance of human teachers. We have had relatively few years of experience and research in developing and using CAL. Moreover, CAL is steadily being improved through a combination of research and improvements based on this research. Thus, we can expect considerable ongoing change in curriculum content, instructional processes, and assessment in the years to come.
As educational computer facilities become routinely available to students, both in schools and at home, schools face the challenge of determining in what ways and to what extent they should teach students to make use of computers to solve the problems and accomplish the tasks that they are studying. You have heard about open book tests. What are your thoughts on open Web-connected tests?
The next newsletter provides a brief history of computers. Likely this will be an appendix in my emerging book, ICTing and Mathing Across the History Curriculum.
Anderson, K. (11/26/2018). How Intelligent Tutoring Systems make deep learning possible. EdSurge. Retrieved 4/26/2020 from https://www.edsurge.com/news/2018-11-26-how-intelligent-tutoring-systems-make-deep-learning-possible.
Best, J. (9/9/2013). IBM Watson: The inside story of how the Jeopardy-winning supercomputer was born, and what it wants to do next. Tech Republic. Retrieved 4/19/2020 from https://www.techrepublic.com/article/ibm-watson-the-inside-story-of-how-the-jeopardy-winning-supercomputer-was-born-and-what-it-wants-to-do-next/.
Chick, N. (n.d.). Metacognition. Vanderbilt Center for Teaching. Retrieved 4/21/2020 from https://cft.vanderbilt.edu/guides-sub-pages/metacognition/.
Eccles, J.S. (July, 2015). Presidential column. Developmental Psychologist. Retrieved 5/2/2020 from https://www.apadivisions.org/division-7/publications/newsletters/developmental/2015/07/metacognition-children.
Friedman, M. (11/25/2014). Patrick Suppes, Stanford philosopher, scientist and Silicon Valley entrepreneur, dies at 92. Stanford News. Retrieved 4/26/2020 from https://news.stanford.edu/news/2014/november/patrick-suppes-obit-112514.html.
Graycaps (n.d.). 6 learning theories every educator must know. The Teacher. Retrieved 4/19/2020 from https://greycaps.com/theteacher/Community/6-learning-theories.
Johnson, Andrew (11/10/2015). Teaching is a science, an art, and a craft. Linkedin. Retrieved 5/1/2020 from https://www.linkedin.com/pulse/teaching-science-art-craft-andrew-johnson/.
Kauer, A. (4/30/2018). What is the earliest age that children are really aware of their own learning ability? Ask Chat. Retrieved 4/21/2020 from https://learning.imascientist.org.uk/question/i-am-interested-in-meta-cognition-what-is-the-earliest-age-that-children-are-really-aware-of-their-own-learning/.
Lederman, D. (1/16/2019). Why MOOCs didn’t work, in 3 data points. Inside Higher Education. Retrieved 4/23/2020 from https://www.insidehighered.com/digital-learning/article/2019/01/16/study-offers-data-show-moocs-didnt-achieve-their-goals.
Lederman, D. (2/14/2018). MOOCs: Fewer new students, but more are paying. Inside Higher Education. Retrieved 4/23/2020 https://www.insidehighered.com/digital-learning/article/2018/02/14/moocs-are-enrolling-fewer-new-students-more-are-paying-courses.
Luehrmann, A. (2002). Should the computer teach the student, or vice-versa? CITE Journal. Retrieved 4/25/2020 from https://citejournal.org/vol2/iss3/seminal/article2.cfm.
McCall, J. (2011). Jeremiah McCall on using simulation games in the history classroom. National History Education Clearinghouse. Retrieved 5/8/2020 from https://teachinghistory.org/nhec-blog/25117. Also see his current (5/8/2020) website at https://gamingthepast.net/.
MOOC List (2020). Teacher education MOOCs and free online courses. Coursera. Retrieved 4/25/2020 from https://www.mooc-list.com/tags/teacher-education.
Moursund, D. (September, 2002). Getting to the second order: Moving beyond amplification uses of information and communications technology in education. Learning and Leading with Technology. Retrieved 4/22/2020 from https://pages.uoregon.edu/moursund/dave/Article&Presentations/second_order.htm.
My MOOC (2020). My MOOC Newsletter. Retrieved 4/25/2020 from https://www.my-mooc.com/en/categorie/education-and-teaching.
National Research Council (1999). Improving student learning. Institute of Education Sciences. Retrieved 5/1/2020 from https://ies.ed.gov/director/board/reports/20096011/index.asp.
Papert, S. (2001). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books. Retrieved 4/25/2020 as a free PDF file from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1ved=2ahUKEwiQ1vac8oTpAhV_
Scratch (2020). Scratch programming language. Scratch.mit.edu. Retrieved 4/25/2020 from https://scratch.mit.edu.
Suppes, P. (1988). Computer-assisted instruction. The Encyclopedia of Educational Media Communications and Technology. Retrieved 4/25/2020 from https://suppes-corpus.stanford.edu/sites/g/files/sbiybj7316/f/computer-assisted_instruction_278.pdf.
Taylor, R. (1980). The Computer in the school: Tutor, tool, tutee. Totowa, NJ: Teachers College Press.
Wikipedia (2020a). Logo (programming language). Retrieved 5/5/2020 from https://en.wikipedia.org/wiki/Logo_(programming_language).
Wikipedia (2020b). Massive open online courses. Retrieved 5/3/2020 from https://en.wikipedia.org/wiki/Massive_open_online_course.
Wink, C. (2/15/2011). ENIAC: 10 things you should know about the original super computer 65 years later. Technical.ly. Retrieved 4/19/2020 from https://technical.ly/philly/2011/02/15/eniac-10-things-you-should-know-about-the-original-modern-super-computer-65-years-later/.
David Moursund is an Emeritus Professor of Education at the University of Oregon, and editor of the IAE Newsletter. His professional career includes founding the International Society for Technology in Education (ISTE) in 1979, serving as ISTE’s executive officer for 19 years, and establishing ISTE’s flagship publication, Learning and Leading with Technology (now published by ISTE as Empowered Learner). He was the major professor or co-major professor for 82 doctoral students. He has presented hundreds of professional talks and workshops. He has authored or coauthored more than 60 academic books and hundreds of articles.