Learning styles: Individualizing computer‐based learning environments

In spite of its importance, learning style is a factor that has been largely ignored in the design of educational software. Two issues concerning a specific set of learning styles, described by Honey and Mumford (1986), are considered here. The first relates to measurement and validity. This is discussed in the context of a longitudinal study to test the predictive validity of the questionnaire items against various measures of academic performance, such as course choice and level of attainment in different subjects. The second issue looks at how the learning styles can be used in computer‐based learning environments. A re‐examination of the four learning styles (Activist, Pragmatist, Reflector and Theorist) suggests that they can usefully be characterized using two orthogonal dimensions. Using a limited number of pedagogical building blocks, this characterization has allowed the development of a teaching strategy suitable for each of the learning styles. Further work is discussed, which will use a multi‐strategy basic algebra tutor to assess the effect of matching teaching strategy to learning style

The relationship between computer interaction and individual user characteristics

Development of effective human computer interaction is being approached independently by two disciplines -- user interface design and computer aided instruction. The lack of communication between the two fields has left each separately pursuing different paths toward the same goals. This thesis attempts to bridge the gap between these two disciplines. An exploratory study was conducted to analyze whether user choices in a computer aided instruction environment and personality types as defined by the Myers-Briggs type indicator are related strongly enough to provide the basis for future user models. The results demonstrated that no single instructional strategy was preferred, implying the need for more than one user model. The amount of instruction chosen did not increase performance. These conclusions have impact on research efforts to understand how both user and system characteristics influence the use of computer technology. The current research efforts to incorporate artificial intelligence techniques by both user interface designers and computer aided instruction developers has heightened the need for knowledge-based systems incorporating interdisciplinary research efforts

Communication, Affect, & Learning in the Classroom

The purpose of the handbook was to synthesize the first three decades of research in instructional communication into a single volume that could help both researchers and instructors understand the value of communication in the instructional process.Preface1.Teaching As a Communication Process The Instructional Communication Process The Teacher The Content The Instructional Strategy The Student The Feedback/Evaluation The Learning Environment/Instructional Context Kibler’s Model of Instruction The ADDIE Model of Instructional Design2.Communicating With Instructional Objectives Why Some Teachers Resent Objectives The Value of Objectives What Objectives Should Communicate3.Instructional Communication Strategies The Teacher As a Speaker The Teacher As a Moderator The Teacher As a Trainer The Teacher As a Manager The Teacher As a Coordinator & Innovator4.Communication, Affect, and Student Needs Measuring Student Affect Basic Academic Needs of Students Traditional Interpersonal Need Models Outcomes of Meeting Student Needs5.Learning Styles What is Learning Style? Dimensions of Learning Style and Their Assessment Matching, Bridging, and Style-Flexing6.Classroom Anxieties and Fears Communication Apprehension Receiver Apprehension Writing Apprehension Fear of Teacher Evaluation Apprehension Classroom Anxiety Probable Causes of Classroom Anxiety Communication Strategies for Reducing Classroom Anxiety7.Communication And Student Self-Concept Student Self-Concept: Some Definitions Characteristics of the Self Development of Student Self-Concept Dimensions of Student Self-Concept Self-Concept and Academic Achievement Effects of Self-Concept on Achievement Poker Chip Theory of Learning Communication Strategies for Nurturing and Building Realistic Student Self-Concept8.Instructional Assessment:Feedback,Grading, and Affect Defining the Assessment Process Evaluative Feedback Descriptive Feedback Assessment and Affect Competition and Cooperation in Learning Environments9.Traditional and Mastery Learning Systems Traditional Education Systems Mastery Learning Modified Mastery Learning10.Student Misbehavior and Classroom Management Why Students Misbehave Categories of Student Behaviors Students’ Effects on Affect in the Classroom Communication, Affect, and Classroom Management Communication Techniques for Increasing or Decreasing Student Behavior11.Teacher Misbehaviors and Communication Why Teachers Misbehave Common Teacher Misbehaviors Implications for the Educational Systems12.Teacher Self-Concept and Communication Dimensions of Teacher Self-Concept Development of Teacher Self-Concept Strategies for Increasing Teacher Self-Concept13.Increasing Classroom Affect Through Teacher Communication Style Communicator Style Concept Types of Communicator Styles Teacher Communication Style Teacher Communicator Behaviors That Build Affect14.Teacher Temperament in the Classroom Four Personality Types Popular Sanguine Perfect Melancholy Powerful Choleric Peaceful Phlegmatic Personality Blends15.Teacher Communication: Performance and Burnout Teaching: A Multifaceted Job Roles of an Instructional Manager Teacher Burnout Symptoms of Teacher Burnout Causes of Teacher Burnout Methods for Avoiding Burnout Mentoring to Prevent BurnoutAppendix A To Mrs. Russell: Without You This Never Would Have HappenedGlossaryInde

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria

User characteristics in intelligent tutoring systems

The development of individualized educational environments, to facilitate learning for the diverse population of students in today\u27s secondary school system, has become more prevalent with the increased ease of access to computers that many schools are now enjoying. The use of Computer Aided Instruction is becoming more common as a means for individual tutoring. This thesis explores the problem of individualizing this instruction by analyzing the relationship between preferred teaching methods and computer users personality types, as defined by the Myers-Briggs type indicator and two other unscientific user characteristics. The preferred teaching method was analyzed using various criteria, including user choices, both sequence and quantity, opinion survey, comments, and observation. The results support many of the conclusions formulated in earlier studies, especially those concerning the independence of performance and the quantity of instruction, as well as the need for multiple instructional methodologies due to type differences. These two conclusions, alone, encourage the idea of more individualized instruction and foster the development of Intelligent Tutoring Systems to provide the student with an environment that is most conducive to his/her learning preference

An analysis of the relationship between learning styles and interaction in online discussions in distance education courses at the University of Wisconsin-Stout

Includes bibliographical references

Predicting Student Performance In A Beginning Computer Science Class (Piaget, Personality, Cognitive Style)

Pupose of the Study. The purpose of this study was to determine factors which effectively predict success in a first course for computer science majors. A secondary goal was to provide a model of the successful computer science student in order to improve teaching and learning in the classroom; Procedures. The sample consisted of 58 students enrolled in all three sections of Computer Science I, during Spring semester, 1985. Student characteristics selected included age, sex, previous high school and college grades, number of high school and college mathematics classes, number of hours worked, and whether the job was computer-related or involved programming. A measure of Piagetian cognitive development developed by Kurtz, the Group Embedded Figures Test (GEFT) and the Myers-Briggs Personality Index (MBTI) were administered early in the semester. These measures were correlated with the student\u27s letter grade in the class using both Chi Square and Pearson\u27s Product Moment Coefficient statistical tests; Findings. Significant relationships were found between grade and the students\u27 previous college grades and the number of high school mathematics classes (p \u3c .05). The correlation between grade, and both number of hours worked and working as a programmer, approached significance (p \u3c .10). Both the Group Embedded Figures Test (p \u3c .01) and the measure of Piagetian Intellectual Development stages (p \u3c .05) were also significantly correlated with grade in this rigorous Pascal programming class; While there was no relationship between the personality type and grade, the Myers-Briggs results provided an interesting profile of the computer science major. On the Extroversion-Introversion, Sensing-Intuitive, and Thinking-Feeling indices, the students were considerably more introverted, intuitive and thinking than the population as a whole, though they were close to national norms on the Perception-Judging index. While computer science students were somewhat like engineering students, they more strongly resembled chess players, when these results were compared with other studies

The Impact of Personality Type on Undergraduate College Student Success at Oklahoma State University

The purpose of this descriptive/causal-comparative study was to determine if relationships existed between individual personality types as determined by the Do What You Are (DWYA) on-line personality inventory and gender, ethnicity, area of academic study, entering and exiting grade point averages (GPA), and time to degree completion of undergraduate students at the case study institution. Data were collected over a six year period by the institution\u27s career development center. The student respondents were undergraduates and were self-selected to take the inventory. The sample included 2, 533 undergraduate students surveyed between 2003 and 2007. Statistical analysis utilized scores on the four continuous dimension scales on the personality inventory and other student demographic variables. Student scores on the DWYA served as the chief independent or predictor variable for all of the outcome variables. The first and second research questions examined the descriptive information of the majority types in each of the academic areas. The third and fourth questions examined the relationship between personality type and undergraduate grade point averages of the respondents. The fifth question examined the relationship between personality type and the student\u27s academic status (continuing, dropped, or graduated). The sixth question sought to find a correlation between personality type and the time to degree obtainment. The four-way factorial ANOVA found one significant main effect interaction between the judging / perceiving dimension scale where judging types had a significantly higher mean GPA than perceiving types. ANOVA also discovered a significant two-way interaction between mean GPA\u27s of the respondents and the extroversion/introversion scale and the thinking/feeling scale. Introverted thinkers had a higher mean GPA than extroverted thinkers. The Chi square statistic was found to be significant for feeling perceiving (FP) personality types (ENFP, ESFP, INFP, ISFP) and the dropout status

Analysis of data on retention of high school band students

Includes bibliographical references