Faculty Teaching Goals at Senior Research Universities
Johnson, Lisa Dawn
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The purpose of this study was to identify faculty teaching goals at senior research universities and to determine variables which could be used to predict these goals. The eight disciplines used in this study were: accounting, chemistry, computer science, economics, English, math, mechanical engineering, and psychology. The independent variables were: gender, academic rank, course level, tenure, and four Biglan categories (pure-hard, pure-soft, applied-hard, and applied-soft). Using the Teaching Goals Inventory (Angelo & Cross, 1993), the teaching goals and primary teaching role of faculty at senior research universities across eight academic disciplines were identified (N = 352). Through a principal axis factor analysis, seven factors emerged for the data gathered from the Teaching Goals Inventory, results of which were slightly different from that of previous research. Further analyses indicated differences between the teaching goals and primary teaching role of faculty at senior research universities, community colleges, and four-year colleges. Seven one-way ANOVAs and subsequent post-hoc comparisons were conducted which indicated significant differences (p < .05) among the factor means across the eight academic disciplines. Comparisons of the primary teaching role across the eight academic disciplines also indicated significant differences (p < .05) in the percentage of faculty selecting each of the six roles. Furthermore, this study provides additional evidence to support the theory that the Biglan categories help explain the differences in teaching goals across academic disciplines. Significant differences (p < .05) were detected in the teaching goals and primary teaching role of senior research university faculty across the four Biglan categories. Through regression analyses, three of the four Biglan categories, gender, and level of course entered as predictors of teaching goals. Academic rank and tenure did not enter into any of the regression equations; however, further analyses indicated these variables were intercorrelated with several other independent variables. Implications for these findings are discussed.
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