The Influence of Instructor-Student Rapport on Instructors’ Professional and Organizational Outcomes     

Brandi N. Frisby  Anna-Carrie Beck  Audrey Smith Bachman  Cynthia Byars  Cynthia Lamberth  Jason Thompson 《Communication Research Reports》2016,33(2):103-110

Students and teachers have relational goals and needs, yet little research has examined the relational goals of instructors and how achieving those goals may affect instructor outcomes. This study examined instructors’ perspectives on instructor-student rapport. Results revealed that instructors who build rapport with students are likely to experience teaching satisfaction, affective commitment to the institution, and enhanced teaching efficacy. However, instructor-student rapport was not related to instructor morale. The findings in this study highlight the importance of positive instructor-student relationships in the classroom and the implications for both instructors and administrators.  相似文献   

DYNAMICS OF ENGAGEMENT AND DISAFFECTION IN A SOCIAL STUDIES CLASSROOM CONTEXT          下载免费PDF全文

Ana M. Taboada Barber  Michelle M. Buehl  Jori S. Beck 《Psychology in the schools》2017,54(7):736-755

In this investigation, we replicated Skinner et al.’s study of the dynamics of engagement with a more diverse sample of Grades 6 and 7 students from a middle school with a large English learner (primarily Spanish‐speaking) student population. We tested dimensions of the self‐system model of motivational development in a specific academic domain (i.e., social studies). Some relationships found by Skinner and colleagues were supported, whereas others were not. Emotional engagement predicted changes in behavioral engagement and disaffection. The classroom context and students’ self‐perceptions predicted changes in engagement and disaffection. Students’ self‐perceptions also mediated the relation between teacher support and engagement and disaffection. None of these relations were moderated by language status.  相似文献   

Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools     

William Grisham  Natalie A. Schottler  Joanne Valli-Marill  Lisa Beck  Jackson Beatty 《CBE life sciences education》2010,9(2):98-107

This completely computer-based module''s purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information''s Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor''s manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics.  相似文献   

A Sporting Dilemma and Its Jurisprudence     

Patrick Lenta  Simon Beck 《体育哲学杂志》2013,40(2):125-143

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Four spheres of knowledge required: an international study of the professional development of literacy/English teacher educators     

Clare Kosnik  Lydia Menna  Pooja Dharamshi  Cathy Miyata  Yiola Cleovoulou  Clive Beck 《师资教育杂志》2015,41(1):52-77

This paper reports on a research study of 28 literacy teacher educators (LTE) in four countries: Canada, the USA, England and Australia. It identifies three main forms of professional development: informal, formal and communities of practice and four spheres of knowledge: research; pedagogy of higher education; literacy and literacy teaching; and current school district and government initiatives. The study reveals the sheer scale of knowledge required to be an effective LTE and demonstrates how participants used the three forms of professional development to enhance their knowledge in each sphere. It concludes that the professional development of teacher educators is not conducted systematically; rather, it is quite ad hoc with much of it occurring through learning while doing. All had to construct their own programmes for professional development, which added yet another layer to their responsibilities.  相似文献   

Development of an Augmented Reality Game to Teach Abstract Concepts in Food Chemistry          下载免费PDF全文

Philip G. Crandall  Robert K. Engler III  Dennis E. Beck  Susan A. Killian  Corliss A. O'Bryan  Nathan Jarvis  Ed Clausen 《Journal of Food Science Education》2015,14(1):18-23

One of the most pressing issues for many land grant institutions is the ever increasing cost to build and operate wet chemistry laboratories. A partial solution is to develop computer‐based teaching modules that take advantage of animation, web‐based or off‐campus learning experiences directed at engaging students’ creative experiences. We used the learning objectives of one of the most difficult topics in food chemistry, enzyme kinetics, to test this concept. Students are apprehensive of this subject and often criticize the staid instructional methods typically used in teaching this material. As a result, students do not acquire a useful background in this important subject. To rectify these issues, we developed an interactive augmented reality application to teach the basic concepts of enzyme kinetics in the context of an interactive search that took students to several locations on campus where they were able to gather raw materials and view videos that taught the basics of enzyme kinetics as applied to the production of high fructose corn syrup (HFCS). The students needed this background to prepare for a mock interview with an HFCS manufacturer. Students and instructors alike found the game to be preferable to sitting in a classroom listening to, or giving, a PowerPoint presentation. We feel that this use of gaming technology to teach difficult, abstract concepts may be a breakthrough in food science education and help alleviate the drain on administrative budgets from multiple wet labs.  相似文献   

Interactions Are Critical     

Christopher W. Beck  Nancy G. Bliwise 《CBE life sciences education》2014,13(3):371-372

Although we agree with Theobold and Freeman (2014) that linear models are the most appropriate way in which to analyze assessment data, we show the importance of testing for interactions between covariates and factors.To the Editor:Recently, Theobald and Freeman (2014) reviewed approaches for measuring student learning gains in science, technology, engineering, and mathematics (STEM) education research. In their article, they highlighted the shortcomings of approaches such as raw change scores, normalized gain scores, normalized change scores, and effect sizes when students are not randomly assigned to classes based on the different pedagogies that are being compared. As an alternative, they propose using linear regression models in which characteristics of students, such as pretest scores, are included as independent variables in addition to treatments. Linear models that include both continuous and categorical independent variables are often termed analysis of covariance (ANCOVA) models. The approach of using ANCOVA to control for differences in students among treatments groups has been suggested previously by Weber (2009) . We largely agree with Theobald and Freeman (2014) and Weber (2009) that ANCOVA models are an appropriate method for situations in which students cannot be randomly assigned to treatments and controls. However, in describing how to implement linear regression models to examine student learning gains, Theobald and Freeman (2014) ignore a fundamental assumption of ANCOVA.ANCOVA assumes homogeneity of slopes (McDonald, 2009 ; Sokal and Rohlf, 2011 ). In other words, the slope of the relationship between the covariate (e.g., pretest score) and the dependent variable (e.g., posttest score) is the same for the treatment group and the control. This assumption is a strict assumption of ANCOVA in that violations of this assumption can result in incorrect conclusions (Engqvist, 2005 ). For example, in Figure 1, both pretest score and treatment have statistically significant main effects in a linear model with only pretest score (F(1, 97) = 25.6, p < 0.001) and treatment (F(1, 97) = 42.6, p < 0.01) as independent variables. Therefore, we would conclude that all students in the class with pedagogical innovation had significantly greater posttest scores than those students in the control class for a given pretest score. Furthermore, we would conclude that the pedagogical innovation led to the same increase in score for all students in the treatment class, independent of their pretest scores. Clearly, neither of these conclusions would be justified.Researchers must first test the assumption of the homogeneity of slopes by including an interaction term (covariate × treatment) in their linear model (McDonald, 2009 ; Weber 2009 ; Sokal and Rohlf, 2011 ). For example, if we measured student achievement in two courses with different instructional approaches in a typical pretest/posttest design, then the interaction between students’ pretest scores and the type of instruction must be considered, because the instruction may have a different effect for high- versus low-achieving students. If multiple covariates are included in the linear model (see Equation 1 in Theobald and Freeman, 2014 ), then interaction terms need to be included for each of the covariates in the model. If the interaction term is statistically significant, this suggests that the relationship between the covariate and the dependent variable is different for each treatment group (F(1, 96) = 25.1, p < 0.001; Figure 1). As a result, the effect of the treatment will depend on the value of the covariate, and universal statements about the effect of the treatment are not appropriate (Engqvist, 2005 ). If the interaction term is not statistically significant, it should be removed from the model and the analysis rerun without the interaction term. Failure to remove an interaction term that was not statistically significant also can lead to an incorrect conclusion (Engqvist, 2005 ). Whether there are statistically significant interactions between the “treatment” and the covariates in the data set used by Theobald and Freeman (2014) is unclear.Open in a separate windowFigure 1.Simulated data to demonstrate heterogeneity of slopes. Pretest values were generated from random normal distributions with mean = 59.8 (SD = 18.1) for the treatment course and mean = 59.3 (SD = 17.0) for the control course, based on values given in Theobald and Freeman (2014) . For the treatment course, posttest values were calculated using the formula posttest_i = 80 + 0.1 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). For the control course, posttest values were calculated using the formula posttest_i = 42 + 0.5 × pre-test_i + Ɛ_i, where Ɛ_i was selected from a random normal distribution with mean = 0 (SD = 10). n = 50 for both courses.In addition to being a strict assumption of ANCOVA, testing for homogeneity of slopes in a linear model is important in STEM education research, as slopes are likely heterogeneous for several reasons. First, for many instruments used in STEM education research, high-achieving students score high on the pretest. As a result, their ability to improve is limited due to the ceiling effect, and differences between treatment and control groups in posttest scores are likely to be minimal (Figure 1). In contrast, low-achieving students have a greater opportunity to change their scores between their pretest and posttest. Second, pedagogical innovations are more likely to have a greater impact on the learning of lower-performing students than higher-performing students. For example, Beck and Blumer (2012) found statistically greater gains in student confidence and scientific reasoning skills for students in the lowest quartile as compared with students in the highest quartile on pretest assessments in inquiry-based laboratory courses.Theobald and Freeman (2014, p. 47) note that “regression models can also include interaction terms that test whether the intervention has a differential impact on different types of students.” Yet, we argue that these terms must be included and only should be excluded if they are not statistically significant.  相似文献   

Overview and other views     

Michael D. Beck 《Publishing Research Quarterly》1985,1(3):36-46

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Aeschylus: Playwright Educator     

Robert H. Beck 《Educational theory》1987,37(1):1-15

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A Reply to Jeffrey L. Zorn     

Robert H. Beck 《Educational theory》1987,37(2):187-187

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