Teacher Knowledge for Active-Learning Instruction: Expert–Novice Comparison Reveals Differences

Auerbach, Anna Jo; Higgins, Margot; Andrews, Tessa C.

doi:10.1187/cbe.17-07-0149

Cited by 50 publications

(46 citation statements)

References 57 publications

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“…This illustrates, as already indicated above, that the role of the teacher in the (inquiry) learning process can be indispensable (see, e.g., Andrews, Leonard, Colgrove, & Kalinowski, ; Auerbach, Higgins, Brickman, Andrews, & Labov, ; Furberg, ), yet this role is neglected in many experimental studies. In the best case, the same teacher teaches the inquiry and the control conditions, but most often, there is hardly any information on the quality of the teaching process.…”

Section: Effectiveness Of (Technology‐based) Inquiry Learningmentioning

confidence: 64%

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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What is the best approach to educating students is, evidently, the pivotal question in educational research. In the general debate on this question, clear positions are often taken―for example, whether teacher‐led instruction or more student‐directed approaches should be followed. In the current narrative review, this central question and the different stances taken are explored for a specific field of learning, STEM (science, technology, engineering, and mathematics) domains. The current article starts with an argument as to why the traditional, more directive teacher‐led educational approach to STEM learning may not always lead to deep conceptual knowledge and proposes exploring more engaged forms of learning. More specifically, a particular arrangement for engaged learning, inquiry learning, is taken as an example in this exploration. It is argued that the effects of educational methods are influenced by a multitude of (interacting) factors related to student and teacher characteristics, context, domain, frequency of use, timing, and probably more. Therefore, this article tries to outline a more nuanced and balanced stance towards choosing educational approaches. A picture is presented to show that adopting and combining different approaches, at both the lesson and the curricular levels, may be necessary to reach optimal levels of learning. Existing and upcoming technologies may play a decisive role in realizing this more complex, but also more realistic view of learning in STEM.

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Section: Effectiveness Of (Technology‐based) Inquiry Learningmentioning

confidence: 64%

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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“…Thus, we can talk about a pedagogy focused on pupil/student, who becomes the actor in the educative act, in elaborating regulations and rules regarding the work rhythm, learning style, grid for reading reality determined by particularities of personality, abilities for adapting and social affirmation. Active-learning instructors are aware of best practices related to students motivation (Auerbach, Higgins, Brickman and Andrews, 2018). The pupil/student is supported to develop autonomy, a pedagogic concept with multiple educative dimensions, correlated with activation, freedom of choice, responsibility, critical thinking, meta-cognition.…”

Section: Active Learning -A Results Of the Professor-student Collaboramentioning

confidence: 99%

Active Learning and Development of Design Thinking Ability at Students

Jitaru

2019

Review of Artistic Education

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The paper analyzes paradigms that highlight the role of active teaching and active learning methods in developing the skills for the professional and social life of the students. The first chapter of the paper argues the need to develop social and creative abilities that lead students to competence in design thinking. Successful response of the student to the demands of professional and social life requires skills like empathy, assertiveness, cooperation, problem solving, implementation of innovative solutions. Ability of design thinking involves a divergent thinking, ability to autonomously design their own strategies in relation to personal development needs, self-assertion and prosocial project development. The training of these skills is possible if the educational environment is one that offers stimulating techniques, adequately prepared human resources, social training contexts. Interactive teaching enables the student to act independently, to be capable of self-management and personal development. The proactive creativity of teachers facilitates active learning methods which will lead to students developing empathy, assertiveness and competence in design thinking.

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“…In discipline-based education research, Poisson regression might be used to determine how student, classroom, or program characteristics influence the number of students from a particular program who persist in a discipline [89] or the number of undergraduates completing research projects within a department [90]. Auerbach and colleagues [54] used Poisson regression to model the number of times an instructional practice, such as promoting metacognition, was noticed by experts compared to novices as they analyzed videos of active-learning classrooms. Andrews and colleagues [55] by counting the number of times a faculty member was reported in a survey as the cause of a change, thus necessitating a Poisson model of their count data.…”

Section: F Poisson Regression 1 When To Use Poisson Regressionmentioning

confidence: 99%

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Theobald

Aikens

Eddy

et al. 2019

Phys. Rev. Phys. Educ. Res.

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling-generalized linear models (glms)-and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher's identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work.

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Teacher Knowledge for Active-Learning Instruction: Expert–Novice Comparison Reveals Differences

Cited by 50 publications

References 57 publications

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Active Learning and Development of Design Thinking Ability at Students

Beyond linear regression: A reference for analyzing common data types in discipline based education research

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