1978•Community Junior College Research QuarterlyRequires access

Individualized Instruction: Extent of Use and Model for Assessing Effectiveness in Community Colleges/Technical Institutes

Mildred Johnson, Barbara Nelle Clawson

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Abstract

Purposes of the study were to (1) determine the extent to which occupational education instructors in postsecondary institutions in North Carolina used individualized instruction; and to (2) develop a model, evaluative measures, and guidelines for use in assessing individualized instruction approaches. An Individualized Instruction Inventory was designed to determine demographic data, practices, and attitudes of occupational education instructors. The sample consisted of instructors who had rated themselves as moderately to highly individualizing instruction. Inventories were returned by 77% of the 354 instructors in the sample. A discriminant analysis was used to determine demographic variables that differentiated (1) between instructors with high and low attitude scores; and (2) among instructors with high, average, and low practice scores. The four components of the model developed were (1) delineate information, (2) plan for data collection, (3) obtain information, and (4) provide information for decision making. Tasks to be completed within each phase were specified, and guidelines for use of the model were written. Evaluative measures appropriate for collecting the desired data were either selected or developed.

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Purposes of the study were to (1) determine the extent to which occupational education instructors in postsecondary institutions in North Carolina used individualized instruction; and to (2) develop a model, evaluative measures, and guidelines for use in assessing individualized instruction approaches. An Individualized Instruction Inventory was designed to determine demographic data, practices, and attitudes of occupational education instructors. The sample consisted of instructors who had rated themselves as moderately to highly individualizing instruction. Inventories were returned by 77% of the 354 instructors in the sample. A discriminant analysis was used to determine demographic variables that differentiated (1) between instructors with high and low attitude scores; and (2) among instructors with high, average, and low practice scores. The four components of the model developed were (1) delineate information, (2) plan for data collection, (3) obtain information, and (4) provide information for decision making. Tasks to be completed within each phase were specified, and guidelines for use of the model were written. Evaluative measures appropriate for collecting the desired data were either selected or developed.

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Available abstract

Purposes of the study were to (1) determine the extent to which occupational education instructors in postsecondary institutions in North Carolina used individualized instruction; and to (2) develop a model, evaluative measures, and guidelines for use in assessing individualized instruction approaches. An Individualized Instruction Inventory was designed to determine demographic data, practices, and attitudes of occupational education instructors. The sample consisted of instructors who had rated themselves as moderately to highly individualizing instruction. Inventories were returned by 77% of the 354 instructors in the sample. A discriminant analysis was used to determine demographic variables that differentiated (1) between instructors with high and low attitude scores; and (2) among instructors with high, average, and low practice scores. The four components of the model developed were (1) delineate information, (2) plan for data collection, (3) obtain information, and (4) provide information for decision making. Tasks to be completed within each phase were specified, and guidelines for use of the model were written. Evaluative measures appropriate for collecting the desired data were either selected or developed.

Key concepts: Sample (material), Plan (archaeology), Medical education, Data collection, Psychology, Linear discriminant analysis, Mathematics education, Computer science

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