Project-based learning is a comprehensive approach to classroom teaching and learning that is designed to engage students in investigation of authentic problems. In this article, we present an argument for why projects have the potential to help people learn; indicate factors in project design that affect motivation and thought; examine difficulties that students and teachers may encounter with projects; and describe how technology can support students and teachers as they work on projects, so that motivation and thought are sustained.

Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique? 2a. Learning conditions 2b. Student characteristics 2c. Materials 2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students' performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students' performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.

https://www.wku.edu/senate/documents/improving_student_learning_dunlosky_2013.pdf

Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology

Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.

/pdf/methods-and-techniques-of-adaptive-hypermedia-3alw86gp8k.pdf

Methods and techniques of adaptive hypermedia

This paper review the 10-year history of tutor development based on the ACT theory (Anderson, 1983,1993). We developed production system models in ACT ofhow students solved problems in LISP, geometry, and algebra. Computer tutors were developed around these cognitive models. Construction ofthese tutors was guided by a set of eight principles loosely based on the ACT theory. Early evaluations of these tutors usually but not always showed significant achievement gains. Best-case evaluations showed that students could achieve at least the same level of proficiency as conventional instruction in one third the time. Empirical studies showed that students were learning skills in production-rule units and that the best tutorial interaction style was one in which the tutor provides immediate feedback, consisting of short and directed error messages. The tutors appear to work better if they present themselves to students as nonhuman tools to assist learning rather than as emulations of human tutors. Students working with these tutors display transfer to other environments to the degree that they can map the tutor environment into the test environment. These experiences have coalesced into a new system for developing and deploying tutors. This system involves first selecting a problem-solving interface, then constructing a curriculum under the guidance of a domain expert, then designing a cognitive model for solving problems in that environment, then building instruction around the productions in that model, and finally deploying the tutor in the classroom. New tutors are being built in this system to achieve the NCTM standards for high school mathematics in an urban setting. (http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA312246)

/pdf/cognitive-tutors-lessons-learned-35qcgk8i4q.pdf

Cognitive Tutors: Lessons Learned

Acknowledgements Introduction 1. Readings and responses 2. Preface to the 1st edition 3. Introduction to the 1st edition 4. Interactive artifacts 5. Plans 6. Situated actions 7. Communicative resources 8. Case and methods 9. Human-machine communication 10. Conclusion to the 1st edition 11. Plans, scripts and other ordering devices 12. Agencies at the interface 13. Figuring the human in AI and robotics 14. Demystifications and re-enchantments of the human-like machine 15. Reconfigurations Notes References.

Human-Machine Reconfigurations: Plans and Situated Actions

Cognitive psychology, artificial intelligence, and computer technology have advanced to the point where it is feasible to build computer systems that are as effective as intelligent human tutors Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP

https://science.sciencemag.org/content/sci/228/4698/456.full.pdf

Intelligent tutoring systems

The tutor for doing proofs in high school geometry consists of a cot of ideal and buggy rules (IRR), a tutor, and an interface. The IBR is responsible for ehiuertly computing matcher, to all the correct and incorrect rules The interface is responsible for interacting with the student and graphically representing the proof. The tutor is responsible for directing the IBR and interface to achieve a current tutorial strategy. The strategy wo employ involves tracing the student's behavior in terms of what rules in the IBR it instantiates, correcting the student when behavior deviates below a minimum threshold, and helping the student over hurdles. While incomplete, current evidence indicates the geometry tutor is guite effective.

The Advanced Computer Tutoring Project has been working on the development of intelligent computerhased tutors for mathematics and science subjects in the range of senior high-school to junior college. This paper describes the general framework that we have developed and its instantiation in the case of a tutor tor generating proofs in geometry. First, we will describe the general philosophy of out tutoring efforts. Second, we will describe the basic structure of the geometry tutor that we have built. Third, we will describe the ongoing efforts to evaluate the tutor.

The geometry tutor

There is often a severe problem of lack of communication between teacher and pupils. When two people communicate, what passes between them are the words and gestures they use to attempt to convey meaning, not the meaning itself. So a teacher has some ideas which he or she hopes to convey by putting them into words, diagrams or symbols. The child may take note of the words, and so on, but from these has to build up a meaning for them. There is clearly a strong possibility that this meaning created by the child is not the meaning intended by the teacher. This possibility is very high if the type of language used by the teacher, or workcard, or textbook writer, is not familiar to the child. Then various things may happen, as Barnes (1986) has so clearly pointed out: a) The child may ignore what the teacher is saying. b) The teacher may ignore what the pupil is saying (the teacher “controls” knowledge by using unfamiliar language, consequently children'S ideas are devalued and are only heard when they talk among themselves). c) The teacher may insist that the pupils use the “correct” words and so, sound scientific. (We, like Barnes, have seen children praised for “thinking like a scientist” when it is clear that the children are simply “making noises which sound scientific”).

Studying conceptual change in learning physics

Using Knowledge Representation to Study Conceptual Change in Students for Teaching Physics

In an effort to uncover fundamental differences between computers and brains, this paper identifies computation with a particular kind of physical process, in contrast to interpreting the behaviors of physical systems as one or more abstract computations. That is, whether or not a system is computing depends on how those aspects of the system we consider to be informational physically cause change rather than on our capacity to describe its behaviors in computational terms. A physical framework based on the notion of “causal mechanism” is used to distinguish different kinds of information processing in a physically-principled way; each information processing type is associated with a particular causal mechanism. The causal mechanism associated with computation is pattern matching, which isphysically defined as the fitting of physical structures such that they cause a “simple” change. It is argued that information processing in the brain is based on a causal mechanism different than pattern matching so defined, implying that brains do not compute, at least not in the physical sense that digital computers do. This causal difference may also mean that computers cannot have mental states.

C. Franklin Boyle

Papers

Intelligent tutoring systems

The geometry tutor

Studying conceptual change in learning physics

Using Knowledge Representation to Study Conceptual Change in Students for Teaching Physics

Computation as an intrinsic property