Teaching Critical Thinking Techniques Strategy Skills Curriculum Planning Packages

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GLN CONSULTING—CURRICULUM PLANNING:

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We offer a variety of curriculum planning packages strategically organized around the use of student engagement in explanatory modeling activities to foster the use of higher-order cognitive skills to think critically about scientific psychology.

Check out the curriculum planning packages below for a structured teaching curriculum that fits your budget, teaching style, student characteristics, and course and departmental needs.

ALL OUR PACKAGES INCLUDE THE FOLLOWING:

Introducing Students to Explanatory Modeling Activities (EMAs)

This feature includes advice on explaining to students what explanatory modeling is and how they can benefit from engagement in these activities.

Ema Engagement

This feature includes advice on developing tasks that can enhance students’ scientific understanding in core topics, integrating engagement in these tasks with teaching methods and instructional resources. We help teachers develop task that resemble the following:

The typical explanatory modeling task

Students are presented with a description of a psychological phenomenon (like a learned response to a configuration of stimuli or the memory of an event) and asked to engage in the following activities:

Construct an explanation of that phenomenon in accordance with the explanatory practices of a given framework in psychology, like behavioral cognitive, or physiological.
Model that explanation as a set of diagrams that capture the pattern of changes produced by causal interactions between those variables that give rise to the phenomenon to be explained.
Use the causal relations captured by the set of diagrams to derive hypotheses about the potential effects of other variables that could alter the manifestations of the phenomenon to be explained.
Design experiments to empirically evaluate these hypotheses.

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Students’ performance on these tasks might be evaluated per the following criteria:

Does the set of diagrams accurately represent those changes produced by interactions between causal variables that contribute to the target phenomenon?
Is the model consistent with well-established, relevant domain knowledge?
Does the model make only those assumptions that are necessary to account for the explanandum phenomenon?
Do the experimental designs adequately control for and/or randomize all possible changes in the explanandum phenomenon other than those intended by the researcher?

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[av_row row_style=” av_uid=’av-wva32′][av_cell col_style=” av_uid=’av-we84a’]Introducing EMAs[/av_cell][av_cell col_style=” av_uid=’av-vx3ga’]✓[/av_cell][av_cell col_style=” av_uid=’av-v6bby’]✓[/av_cell][av_cell col_style=” av_uid=’av-ur8qm’]✓[/av_cell][/av_row]
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[av_row row_style=” av_uid=’av-r18fm’][av_cell col_style=” av_uid=’av-qo2o2′]Strategy Development[/av_cell][av_cell col_style=” av_uid=’av-q0vva’]✓[/av_cell][av_cell col_style=” av_uid=’av-p86zy’]✓[/av_cell][av_cell col_style=” av_uid=’av-osxrq’]✓[/av_cell][/av_row]
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[av_row row_style=” av_uid=’av-idcyq’][av_cell col_style=” av_uid=’av-htbqu’]Scientific Arguments
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[av_row row_style=” av_uid=’av-fgjyy’][av_cell col_style=” av_uid=’av-1ftoe’]Applying Inferences
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Core Concepts

Preparing students for engagement in these activities by presenting them with material about how explanations enhance understanding of phenomena and how scientists often use modeling strategies to organize their reasoning activities and self-generated diagrams to reduce memory load.

Scientific explanations enhance understanding of phenomena by showing how they arise from causal interactions among relevant variables. Explanations may take the form of lawlike generalizations that capture relevant causal dependencies or mechanistic descriptions of causal relations among components of systems of interdependent variables. Law-like generalizations capture relations that are relatively stable across time and space. Mechanisms capture relations that vary as a function of the material properties and spatiotemporal organization of target system components.

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Strategy Development

Providing students with recommended strategies for constructing, refining, and empirically evaluating explanatory models

Recommended strategies include activities like the following:

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Using knowledge of the explanatory practices of a framework in psychology, those variables that are relevant (i.e. make a difference) to the explanandum phenomenon, and constraints on causal interactions among these variables to construct a candidate model.
Representing that explanation as a set of diagrams consisting of nodes representing variables that can take on qualitative and/or quantitative values connected by arrows representing changes in each variable as a function of changes in other variables in the diagram.
Evaluating the fit between model and target by using the nodes and arrows in the diagrams to guide mental simulation of the pattern of interactions that give rise to the explanandum phenomenon. The diagrams facilitate reasoning by reducing the load on working memory through dynamic coupling of items of relevant domain knowledge with relevant features of the diagrams.

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Evaluating the fit between model and target by using the nodes and arrows in the diagrams to guide mental simulation of the pattern of interactions that give rise to the explanandum phenomenon. The diagrams facilitate reasoning by reducing the load on working memory through dynamic coupling of items of relevant domain knowledge with relevant features of the diagrams.
Deriving hypotheses by using these diagrams to guide the mental simulation of potential effects of other variables that could alter antecedent and/or background conditions or the causal interactions among relevant variables.
Designing experiments to empirically evaluate these hypotheses.

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GLN CONSULTING—CURRICULUM PLANNING:

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Check out the curriculum planning packages below for a structured teaching curriculum that fits your budget, teaching style, student characteristics, and course and departmental needs.

ALL OUR PACKAGES INCLUDE THE FOLLOWING:

Introducing Students to Explanatory Modeling Activities (EMAs)

This feature includes advice on explaining to students what explanatory modeling is and how they can benefit from engagement in these activities.

Ema Engagement

The typical explanatory modeling task

Construct an explanation of that phenomenon in accordance with the explanatory practices of a given framework in psychology, like behavioral cognitive, or physiological.
Model that explanation as a set of diagrams that capture the pattern of changes produced by causal interactions between those variables that give rise to the phenomenon to be explained.
Use the causal relations captured by the set of diagrams to derive hypotheses about the potential effects of other variables that could alter the manifestations of the phenomenon to be explained.
Design experiments to empirically evaluate these hypotheses.

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Students’ performance on these tasks might be evaluated per the following criteria:

Does the set of diagrams accurately represent those changes produced by interactions between causal variables that contribute to the target phenomenon?
Is the model consistent with well-established, relevant domain knowledge?
Does the model make only those assumptions that are necessary to account for the explanandum phenomenon?
Do the experimental designs adequately control for and/or randomize all possible changes in the explanandum phenomenon other than those intended by the researcher?

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[av_row row_style=”][av_cell col_style=”]Introducing EMAs[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][/av_row]
[av_row row_style=”][av_cell col_style=”]Core Concepts[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][/av_row]
[av_row row_style=”][av_cell col_style=”]Strategy Development[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][/av_row]
[av_row row_style=”][av_cell col_style=”]EMA Engagement[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][/av_row]
[av_row row_style=”][av_cell col_style=”]Metacognitive Reflection[/av_cell][av_cell col_style=”]✕[/av_cell][av_cell col_style=”]✓[/av_cell][av_cell col_style=”]✓[/av_cell][/av_row]
[av_row row_style=”][av_cell col_style=”]Scientific Arguments
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[av_row row_style=”][av_cell col_style=”]Applying Inferences
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[av_row row_style=”][av_cell col_style=”]Evaluation Techniques
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[av_row row_style=”][av_cell col_style=”]Evaluation Analysis
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[av_row row_style=’avia-pricing-row’][av_cell col_style=”]PRICE[/av_cell][av_cell col_style=”]$200[/av_cell][av_cell col_style=”]$300[/av_cell][av_cell col_style=”]$400[/av_cell][/av_row]
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Core Concepts

Scientific explanations enhance understanding of phenomena by showing how they arise from causal interactions among relevant variables. Explanations may take the form of lawlike generalizations that capture relevant causal dependencies or mechanistic descriptions of causal relations among components of systems of interdependent variables. Law-like generalizations capture relations that are relatively stable across time and space. Mechanisms capture relations that vary as a function of the material properties and spatiotemporal organization of target system components.

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Strategy Development

Providing students with recommended strategies for constructing, refining, and empirically evaluating explanatory models

Recommended strategies include activities like the following:

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Using knowledge of the explanatory practices of a framework in psychology, those variables that are relevant (i.e. make a difference) to the explanandum phenomenon, and constraints on causal interactions among these variables to construct a candidate model.
Representing that explanation as a set of diagrams consisting of nodes representing variables that can take on qualitative and/or quantitative values connected by arrows representing changes in each variable as a function of changes in other variables in the diagram.
Evaluating the fit between model and target by using the nodes and arrows in the diagrams to guide mental simulation of the pattern of interactions that give rise to the explanandum phenomenon. The diagrams facilitate reasoning by reducing the load on working memory through dynamic coupling of items of relevant domain knowledge with relevant features of the diagrams.

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Evaluating the fit between model and target by using the nodes and arrows in the diagrams to guide mental simulation of the pattern of interactions that give rise to the explanandum phenomenon. The diagrams facilitate reasoning by reducing the load on working memory through dynamic coupling of items of relevant domain knowledge with relevant features of the diagrams.
Deriving hypotheses by using these diagrams to guide the mental simulation of potential effects of other variables that could alter antecedent and/or background conditions or the causal interactions among relevant variables.
Designing experiments to empirically evaluate these hypotheses.

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