Introductory Artificial Intelligence Course Research Articles

Effect of groups size on students' learning achievement, motivation, cognitive load, collaborative problem‐solving quality, and in‐class interaction in an introductory AI course

AbstractBackgroundGroup size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem‐solving process.ObjectivesThis study investigated the effect of group size in a K‐12 introductory Artificial Intelligence course by comparing the students' cognitive load, learning motivation, collaborative problem‐solving quality, and in‐classroom interaction between two‐ and three‐student groups.MethodsForty‐eight high school students were randomly assigned to two kinds of groups (i.e., the two‐student group, and the three‐student group, each consisting of 24 students). During the experiment, Xiaofei robots were used to teach the theoretical and practical content of five AI topics over 6 weeks for 1.5 h each week.ResultsThe ANOVA results indicated that group size mattered in the AI course, the two‐student group was more effective in terms of enhancing students' learning motivation and collaborative problem‐solving quality, as well as imposing more cognitive load than the three‐student group. The advantage was more obvious in the practical problem context. The Lag Sequential Analysis results indicated that more collaborative learning behavioural sequences existed in the two‐student group than in the three‐student groups.ContributionThis research provides empirical evidence and potential guidance for group design in Artificial Intelligence Education. Although group size did not affect students' learning achievement, it affected learning motivation, cognitive load, and problem‐solving quality and processes. Two‐student groups work better than three‐student groups in the AI course.

Teaching Introductory Artificial Intelligence through Java-Based Games

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

Performance Evaluation of LSA, NMF and ILSA in Electronic Assessment of Free Text Document

Aims: To evaluate the performance of an Improved Latent Semantic Analysis (ILSA), Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NMF) algorithms in an Electronic Assessment Application using metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence, Assessment Accuracy and Adequacy in Semantic Representation.
 Methodology: The three algorithms were separately applied in developing an Electronic Assessment application. One hundred students’ responses to a test question in an introductory artificial intelligence course were used. Their performance was measured based on the following metrics, Term Similarity, Precision, Recall and F-measure functions, Mean divergence and Assessment Accuracy.
 Results: ILSA outperformed the LSA and NMF with an assessment accuracy of 96.64, mean divergence from manual score of 0.03, and recall, precision and f-measure value of 0.83, 0.85 and 0.87 respectively.
 Conclusion: The research observed the performance of an improved algorithm ILSA for electronic Assessment of free text document using Adequacy in Semantic Representation, Retrieval Quality and Assessment Accuracy as performance metrics. The results obtained from the experimental designs shows the adequacy of the improved algorithm in semantic representation, better retrieval quality and improved assessment accuracy.

Writing and speech instruction in an introductory artificial intelligence course

Providing opportunities in both written and oral communication continues to grow in importance within the undergraduate Computer Science major. An introductory course in Artificial Intelligence tha...

Fuzzy linguistic descriptions for execution trace comprehension and their application in an introductory course in artificial intelligence

Artificial Intelligence is a central topic in the computer science curriculum. From the year 2011 a project-based learning methodology based on computer games has been designed and implemented into the intelligence artificial course at the University of the Bio-Bio. The project aims to develop software-controlled agents (bots) which are programmed by using heuristic algorithms seen during the course. This methodology allows us to obtain good learning results, however several challenges have been founded during its implementation. In this paper we show how linguistic descriptions of data can help to provide students and teachers with technical and personalized feedback about the learned algorithms. Algorithm behavior profile and a new Turing test for computer games bots based on linguistic modelling of complex phenomena are also proposed in order to deal with such challenges. In order to show and explore the possibilities of this new technology, a web platform has been designed and implemented by one of authors and its incorporation in the process of assessment allows us to improve the teaching learning process.

ACTIVE‐ating Artificial Intelligence: Integrating Active Learning in an Introductory Course

This column describes my experience with using a new classroom space (the ACTIVE Center), which was designed to facilitate group‐based active learning and problem solving, to teach an introductory artificial intelligence course. By restructuring the course into a format that was roughly half lecture and half small‐group problem solving, I was able to significantly increase student engagement, their understanding and retention of difficult concepts, and my own enjoyment in teaching the class.

Teaching With Watson


 
 
 In this paper, we describe how we integrated the materials from the 2013 IBM The Great Minds Challenge (TGMC) - Watson Technical Edition into our Introductory Artificial Intelligence course. We describe the variety of materials made available by IBM, as well as the nature of the competition and the datasets that are at the heart of it. We detail how, where and in what form we integrated the materials into our course. We describe assignments that are based on the materials from the competition as well as additional materials we incorporated into our course. We finish by evaluating our experience in teaching with the materials as well as summarize relevant student feedback. We make recommendations for those who wish to adopt the materials.
 
 

Wumpus World in introductory artificial intelligence

With an emphasis toward hands-on learning of Artificial Intelligence (AI) concepts, we present an overview of an introductory AI course that uses a series of five projects based in the game of Wump...

Teaching AI through machine learning projects

An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.

Poker as a group project for artificial intelligence

Assigning the development of a poker-playing agent as a group project allows flexibility with respect to the topics and techniques typically covered in an introductory Artificial Intelligence course. A poker agent project also provides students the experience of 'authentic' AI research, due to the status of poker as an 'unsolved' problem in AI. Despite this status, a poker agent project is feasible for a semester, half-semester, or quarter-long group project. Problems in assigning group projects are also considered, as are suggestions for mitigating these problems.

Teaching Introductory Artificial Intelligence Using a Simple Agent Framework

This paper describes a flexible method of teaching introductory artificial intelligence (AI) using a novel, Java-implemented, simple agent framework developed specifically for the purposes of this course. Although numerous agent frameworks have been proposed in the vast body of literature, none of these available frameworks proved to be simple enough to be used by first-year students of computer science. Hence, the authors set out to create a novel framework that would be suitable for the aims of the course, for the level of computing skills of the intended group of students, and for the size of this group of students. The content of the introductory AI course in question is a set of assignments that requires the students to use intelligent agents and other AI techniques to monitor, filter, and retrieve relevant information from the World Wide Web. It represents, therefore, a synthesis of the traditional objectivist approach and a real-world-oriented, constructivist approach to teaching programming to novices. The main aim of implementing such a pedagogy was to engage the students in learning to which they personally relate while attaining intellectual rigor. Classroom experience indicates that students learn more effectively when the traditional objectivist approach is combined with a constructivist approach than when this orthodox approach to teaching programming to novices is used alone.

Accommodating field-dependence

In a previous study, it was found that field-independent learners performed significantly better than field-dependent learners in a web based and in a computerised text-based environment. It was decided to adapt both a web interface and a traditional text to accommodate the Field-dependent learner so as to try to reduce the disparity in performance with Field-independents. Seventy Information Systems students were initially assessed for Field-dependence/Field-independence cognitive style using the Group Embedded Figures Test (GEFT). The group of students was divided up randomly into two halves. In Session one, group 1 studied using a Web interface Modules 1 - 9 of an Introductory Course in Artificial Intelligence. Group 2 studied the same material in hard copy form only (Text). All studied the material for one hour per week for six weeks. They were then assessed in a one-hour exam. After a period of 7 weeks, in Session 2, Group 1 studied Modules 10 - 18 using Text while group two studied the same material using the Web interface. The results showed that the performance of Field-dependent individuals, be it in text or web, to be comparable to Field-independents.

Foundational Aspects of Student‐Controlled Learning: A Paradigm for Design, Development, and Assessment Appropriate for Web‐Based Instruction

AbstractThis paper presents a strategy for the design and organization of materials for Web‐based instruction (WBI) founded upon cognitive modeling for the identification and organization of the major concepts in the domain of interest, based upon the Pathfinder paradigm. The original purpose of the Pathfinder paradigm was to model aspects of human semantic (associative) memory. A brief introduction to the Pathfinder paradigm is presented, and the rationale for its use in WBI is discussed. The development of this paradigm for WBI, in the context of eliciting and representing knowledge from domain experts, and its use in a pilot study is described. The domain used for the pilot study was the A* search algorithm, embedded within an introductory course in artificial intelligence. Assessment of the paradigm is also discussed, and preliminary methods are applied to the pilot study.

Equilibriating instructional media for cognitive styles

Two types of instructional (Text and Web) that had been used in a previous study [1] were adapted to accommodate Cognitive Style preferences for Witkin's Field-dependent [2] and Riding's Imager [3]. Ninety six Information Systems students were randomly allocated to each of these environments and their Cognitive Styles were assessed. The students studied an Introductory Course in Artificial Intelligence one hour per week for six weeks after which they were assessed by a one hour exam. It was found that Field-dependents and Field-independents performed similarly in both environments as indicated by their examination scores demonstrating the success of the adaption of both environments for Field-dependents. The adaptation for Imagers as measured by Riding's CSA [3] was not successful as Verbalisers performed better than Imagers in both Text and Web. This raises questions about the stability of Riding's Verbaliser/Imager dimension. People performed significantly better in the Text environment than in the Web environment.

Do cognitive styles affect learning performance in different computer media?

The impact of three treatments Text, CD-ROM, Internet site and student Cognitive Styles on learning performance was investigated. Prior to the commencement of the course the students completed the following psychometric tests: the Group Embedded Figures Test (GEFT) [1], Kirton's Adaptive-Innovator Inventory (KAI) [2] and Cognitive Styles Analysis (CSA) [3]. The subject matter was an Introductory Course in Artificial Intelligence. Forty-seven final year Information System students participated, randomly assigned within sexes to one of the three treatments. Upon completion of six one-hour sessions they were given a sixty-minute examination to assess overall learning.It was found that only Field Dependence-Field Independence interacted with overall learning performance at a statistically significant level (p 0.001) irrespective of treatment. When the three treatments were investigated separately the results suggest that Verbalisers performed better than Imagers in the Internet treatment, while the Analytics performed better than the Wholists in the CD-ROM treatment. The results of the study are discussed in terms of individual differences (Cognitive Styles) together with implications for Web site design.

Evolutionary artificial intelligence

Several challenges confront the organizer of an introductory course in artificial intelligence (AI). First, one has to decide what subject matter to include. The union of everything in all of the popular AI textbooks is much too large, and the intersection undoubtedly won't include enough of what the organizer thinks important. The second challenge is how to blend the selected topics into a coherent whole. The third involves matters such as problem sets, programming exercises, laboratory work, case studies, and collateral readings. Finally, one must decide on the main purpose of the course: is it to teach AI techniques and skills, or is it to study AI's intellectual content, perhaps presenting related topics in psychology and philosophy? In this note we concentrate on the first and second of these topics---how to present a coherent view of the core subject matter of AI.

A consideration of some approaches to course organization

An introductory artificial intelligence course may be taught with emphasis on theory and formalism ("science"), or with emphasis on designing and building artifacts that display intelligence ("engineering"). Along another dimension, it makes pedagogical sense to organize the course around a central theme, but the course may instead be taught as a smorgasbord of approaches and problems. This paper briefly considers the four possible combinations of approaches.

A repository that supports teaching and cooperation in the introductory AI course

This paper describes the development of FLAIR (Flexible Learning with an Artificial Intelligence Repository), a repository of educational material and of a highly visual computing environment for use in laboratories associated with the introductory undergraduate Artificial Intelligence (AI) course. This repository supports sharing of pedagogic material and of development tools, and cooperation in their use, while allowing diversity in content and in use at different institutions. Thus the development of the repository has stressed the production of system tools, extensible object-oriented libraries, and strong programming frameworks. Some modules currently available are on Search and Automated Reasoning. Examples of the presentation techniques used are conceptual maps, hypertext, and graphic animations of algorithms. Initial experience in the use of the repository in teaching the introductory AI course is taking place in the 1993/94 academic year.

Using the game cube-4 as an example in an introductory artificial intelligence course

Using the game cube-4 as an example in an introductory artificial intelligence course

Book review: The Elements of Artificial Intelligence An Introduction Using LISP by Steven L. Tanimoto (Computer Science Press)

The Elements of Artificial Intelligence: an Introduction using LISP is intended (as stated in the preface) for use as the text for an introductory course in artificial intelligence. The author's approach to the subject is stated on the dust jacket (and in the preface, although not as concisely).