Teaching is an interweave of both instructional and managerial activities. The purpose of this study was (1) to identify and classify classroom management behaviors, (2) to build a model explicating such behaviors, and (3) to outline the implications of this model in instructional research. Because the authors aimed at describing actual classroom management behaviors, believed by the teacher to be rational, the classroom management behavior model can be characterized by the following : 1. It not only describes differences in classroom management behaviors among teachers but also describes those acted out by the same teacher. 2. It describes two types of classroom management behaviors. One type is planned and deliberate. For example, the teacher inserts new materials into the instruction in order to promote the students' intellectual engagement in the lesson. The other is spontaneous. Again, for example, the teacher makes changes in the students' activities or perhaps his or her questioning sequence. This paper also presents two examples of the model in actual use. The first is related to planned management behaviors, the second to spontaneous behaviors.

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In this study, the authors investigate the relationship between learner keyboard input speed and keyboard-based test performance. They also propose countermeasures for learners who are at a disadvantage due to the need to use a keyboard when taking tests. Junior college students were given a keyboard input speed test, a general test requiring answers via a keyboard (computerized testing), and a pencil- and-paper test. Test scores plus learner feelings towards each type of test were used in the study. Other factors used in the analysis of relationships included such learner characteristics as interest in learning English, Japanese text input modes, and internal/external locus of control. The results revealed that (1) in computerized testing, those learners who are slow at the keyboard score below those who are faster, (2) even when keyboard speeds are low, learner feelings towards computerized testing are good when Japanese text input modes (alphabetical versus Japanese kana) are matched to interest in learning English, and (3) matching learner keyboard input modes to interests in learning English based upon (2), above, is of benefit to those who tend to maintain an internal locus of control.

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The purpose of this study was to use Aikyodai's Computer Anxiety Scale (ACAS), developed by the author, to identify those factors which may be significant predictors of computer anxiety among high school students. Type I Quantification theory, a multivariate analysis technique developed by C. Hayashi, was performed using 10 independent predictive variables as factors and computer anxiety scores as measured using the ACAS as an outside criterion variable. The analysis revealed that the multiple correlation (ρ), a measure of predictive efficiency, was 0.652. This indicated that about 42.5% of the variance in computer anxiety could be accounted for by the 10 factors used in the study. Three of these factors, all of which are attitudinal, i. e., computer interest, computer confidence, and an ambivalent attitude towards contributed the most to the prediction of computer anxiety. Also, computer experience and math anxiety were significant predictors. In contrast to the significant contributions of the above relatively unstable factors, more stable factors such as gender and level of intelligence contributed less to the variance of computer anxiety. In closing, the author also discusses the implications of these findings in the fostering of education for computer literacy.

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Arithmetic assigment statement problems were presented four times of two midterm and two final examinations. Each problem consisted of a number of short questions. But they also differed from each other in types of left variable and equation types. The resulting incorrect answers were divided into one of nine categories. The number of incorrect answers decreased as the exercise were repeated until all of the students were able to give correct answers in all short questions. All answers were classified as either correct, incorrect or nonsensical and the students were grouped into high, average or lower ability based upon the scores received in the first examination. Using the student ability groups as an external standard, quantification theory was used to analyze the responses by category. It was found that students could be clearly distinguished by ability and that questions which had high incorrect response rates or which contributed greatly to distinguishing the ability groups required special individual guidance.

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