Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

This article is a review of experiments comparing the effectiveness of human tutoring, computer tutoring, and no tutoring. “No tutoring” refers to instruction that teaches the same content without tutoring. The computer tutoring systems were divided by their granularity of the user interface interaction into answer-based, step-based, and substep-based tutoring systems. Most intelligent tutoring systems have step-based or substep-based granularities of interaction, whereas most other tutoring systems (often called CAI, CBT, or CAL systems) have answer-based user interfaces. It is widely believed as the granularity of tutoring decreases, the effectiveness increases. In particular, when compared to No tutoring, the effect sizes of answer-based tutoring systems, intelligent tutoring systems, and adult human tutors are believed to be d = 0.3, 1.0, and 2.0 respectively. This review did not confirm these beliefs. Instead, it found that the effect size of human tutoring was much lower: d = 0.79. Moreover, the eff...

The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems

This survey summarizes almost 50 years of research and development in the field of Augmented Reality AR. From early research in the1960's until widespread availability by the 2010's there has been steady progress towards the goal of being able to seamlessly combine real and virtual worlds. We provide an overview of the common definitions of AR, and show how AR fits into taxonomies of other related technologies. A history of important milestones in Augmented Reality is followed by sections on the key enabling technologies of tracking, display and input devices. We also review design guidelines and provide some examples of successful AR applications. Finally, we conclude with a summary of directions for future work and a review of some of the areas that are currently being researched.

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A Survey of Augmented Reality

Computers have transformed every facet of our culture, most dramatically communication, transportation, finance, science, and the economy. Yet their impact has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. Another reason is that current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. The more instructional software can reason about its own teaching process, know what it is teaching, and which method to use for teaching, the greater is its impact on education. 

Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia.

*Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available
*Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet
*Features algorithmic material that enables programmers and researchers to design building components and intelligent systems

Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning

We present the design and an evaluation of Thermo-Tutor, an Intelligent Tutoring System (ITS) that teaches thermodynamic cycles in closed systems. Thermo-Tutor provides opportunities for students to practice their skills by solving problems. When a student submits a solution, Thermo-Tutor analyzes it and provides appropriate feedback. We discuss the support for problem solving, and the student model the ITS maintains. An initial evaluation of Thermo-Tutor was performed at the University of Canterbury. The findings show that the ITS supports student learning effectively.

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Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

Evaluation is an integral part of research that provides a true measure of effectiveness. This paper presents a study conducted to evaluate the effectiveness of CAS, a knowledge acquisition authoring system developed for generating the domain knowledge required for constraint-based tutoring systems with the assistance of a domain expert. The study involved a group of novice ITS authors composing domain models for adding two fractions. The results of the study showed that CAS was capable of generating highly accurate knowledge bases with considerably less effort than the effort required in manual composition.

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Constraint Authoring System: An Empirical Evaluation

For most airlines, there are numerous policies, agreements and regulations that govern the workload of airline crew. Although some constraints are formally documented, there are many others based on established practice and tacit understanding. Consequently, the task of developing a formal representation of the constraints that govern the working conditions of an airline’s crew requires extensive time and effort involving interviews with the airline’s crew schedulers and detailed analysis of historical schedules. We have developed a system that infers crew scheduling constraints from historical crew schedules with the assistance of a domain expert. This system implements the ComCon algorithm developed to learn constraints that prescribe the limits of certain aspects of crew schedules. The algorithm induces complex multivariate constraints based on a set of user provided templates that outline the general structure of important constraints. The results of an evaluation conducted with crew schedules from two commercial airlines show that the system is capable of learning the majority of the minimum rest constraints.

Learning crew scheduling constraints from historical schedules

Design tasks are difficult to teach, due to large, unstructured solution spaces, underspecified problems, non-existent problem solving algorithms and stopping criteria. In this paper, we comment on our approach to develop KERMIT, a constraint-based tutor that taught database design. In later work, we re-implemented KERMIT as EER-Tutor, and extended its instructional domain. Several evaluation studies performed with KERMIT and EER-Tutor show that they are effective Intelligent Tutoring Systems (ITSs). We also comment on various extensions made to EER-Tutor over the years. There are several contributions of our research, such as developing effective problem-solving support for conceptual database design in terms of interface design. Our database design tutors deal with large solution spaces efficiently by specifying constraints that capture equivalent solution states, and using ideal solutions to capture the semantics of the problem. Instead of requiring a problem solver, the ITS checks whether the student’s database schema is correct by matching it to constraints and the ideal solution. Another contribution of our work is in guidelines for developing effective feedback to the student.

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Teaching Database Design with Constraint-Based Tutors

Authoring ITS domain models is a difficult task re quiring many skills. We explored whether modeling Ontology reduces the problem, by g ivin the students of an e-learning summer school the task of developing the model for a simpl e domain in under sixty minutes using ontology. Some students also used our tool to develop a compl ete tutor in around eight hours, which is much faster than they could be expected to author the sy stem without the tool. The results suggest this style of authoring can lead to very rapid ITS devel opment.

Pramuditha Suraweera

Papers

Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

Constraint Authoring System: An Empirical Evaluation

Learning crew scheduling constraints from historical schedules

Teaching Database Design with Constraint-Based Tutors

Domain modelling with ontology: A case study