Macro-level framework - Revision history

Vanlehn at 02:27, 13 April 2007

2007-04-13T02:27:22Z

← Older revision		Revision as of 02:27, 13 April 2007
Line 5:		Line 5:

	== A multi-dimensional framework for the macro level ==		== A multi-dimensional framework for the macro level ==

			[This has not been vetted by Ken, Chuck or the EC. Consider it a personal opinion. -- Kurt]

	Although many classification schemes may make sense for the macro level (PSLC’s [[Root node \| cluster-subcluster hierarchy]] is one), this page presents a multidimensional one. That is, this classification scheme defines a set of instructional dimensions, where each dimension has a set of alternative, non-numeric values. A point in this multidimensional space is simply a specification of a value for each dimension. Each point in the space corresponds to a generic type of instruction. [[Instructional dimensions root \| Click here for a list of the instructional dimensions currently being investigated.]]		Although many classification schemes may make sense for the macro level (PSLC’s [[Root node \| cluster-subcluster hierarchy]] is one), this page presents a multidimensional one. That is, this classification scheme defines a set of instructional dimensions, where each dimension has a set of alternative, non-numeric values. A point in this multidimensional space is simply a specification of a value for each dimension. Each point in the space corresponds to a generic type of instruction. [[Instructional dimensions root \| Click here for a list of the instructional dimensions currently being investigated.]]

Vanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */

2007-04-07T18:52:14Z

Draft version of a multi-dimensional framework for the Macro Level

← Older revision		Revision as of 18:52, 7 April 2007
Line 1:		Line 1:
	~~=== Draft version~~ of a ~~multi-dimensional framework for~~ the ~~Macro Level ===~~		The overarching research goal of the PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it. Roughly aligned with these questions are our two major level of explanation. The [[macro level]], which focuses mostly on identifying the conditions where robust learning occurs. It is based on observable conditions, activities and results. The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning. It is based on inference of unobservable conditions, activities and results.


			Just as medicine finds indispensable both the clinical and biological levels of explanation, the PSLC has found both its levels to be indispensable as well. The macro level is like the clinical level—it is a relatively atheoretical classification of treatments and effects. The micro level is like the biological level—it explains why certain treatments have certain effects.

	~~The overarching research goal of~~ the PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it. Roughly aligned with these questions are our two major level of explanation. The [[macro level]], which focuses mostly on identifying the conditions where robust learning occurs. It is based on observable conditions, activities and results. The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning. It is based on inference of unobservable conditions, activities and results.		== A multi-dimensional framework for the macro level ==

	~~Just as medicine finds indispensable both~~ the ~~clinical and biological levels~~ of ~~explanation~~, ~~the PSLC~~ has ~~found both its levels to be indispensable as well~~. ~~The macro level~~ is ~~like~~ the ~~clinical level—it is~~ a ~~relatively atheoretical classification~~ of ~~treatments and effects~~. ~~The micro level is like~~ the ~~biological level—it explains why certain treatments have certain effects~~.		Although many classification schemes may make sense for the macro level (PSLC’s [[Root node \| cluster-subcluster hierarchy]] is one), this page presents a multidimensional one. That is, this classification scheme defines a set of instructional dimensions, where each dimension has a set of alternative, non-numeric values. A point in this multidimensional space is simply a specification of a value for each dimension. Each point in the space corresponds to a generic type of instruction. [[Instructional dimensions root \| Click here for a list of the instructional dimensions currently being investigated.]]

	Although many classification schemes may make sense for the macro level (PSLC’s cluster-subcluster hierarchy is one), this page presents a multidimensional one. That is, this classification scheme defines a set of design dimension, where each dimension has a set of alternative, non-numeric values. A point in this multidimensional space is simply a specification of a value for each dimension. Each point in the space corresponds to a generic type of instruction.
	~~[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]~~

	Whenever possible, the values of a dimension are ordered or partially ordered by the amount of assistance they offer students, where higher assistance values raise performance during training. For instance, along the feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback. This ordering allows testing the PSLC’s Assistance Hypothesis:		Whenever possible, the values of an instructional dimension are ordered or partially ordered by the amount of [[assistance]] they offer students, where higher assistance values raise performance during training. For instance, along the feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback. This ordering allows testing the PSLC’s [[Assistance Hypothesis]]:

	* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.		* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.

	Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one ~~dimesion~~ and the same values along all ~~others~~. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand ~~a design~~ space with thousands or millions of points.		Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimension and the same values along all other dimensions. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand an instructional space with thousands or millions of points.


			Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related, but it describes describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction. Several micro-level explanations may be consistent with a single macro-level results, so experiments may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to determine which micro-level explanations is more accurate.

	Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different in that it describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.

	[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]		[[Instructional dimensions root\|Click here for a list of the instructional dimensions currently being investigated.]]

Vanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */

2007-04-07T16:39:17Z

Draft version of a multi-dimensional framework for the Macro Level

← Older revision		Revision as of 16:39, 7 April 2007
Line 14:		Line 14:
	Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.		Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.

	Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.		Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different in that it describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.

	[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]		[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]

Vanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */

2007-04-07T16:34:17Z

Draft version of a multi-dimensional framework for the Macro Level

← Older revision		Revision as of 16:34, 7 April 2007
Line 12:		Line 12:
	* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.		* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.

	Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two ~~indepentent~~ variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.		Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.

	Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.		Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.

	[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]		[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]

Vanlehn: /* Draft version of a framework for the Macro Level */

2007-04-07T16:25:39Z

Draft version of a framework for the Macro Level

← Older revision		Revision as of 16:25, 7 April 2007
Line 1:		Line 1:
	=== Draft version of a framework for the Macro Level ===		=== Draft version of a multi-dimensional framework for the Macro Level ===

	The macro level ~~framework~~ is based on ~~a three-part hypothesis~~.		The overarching research goal of the PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it. Roughly aligned with these questions are our two major level of explanation. The [[macro level]], which focuses mostly on identifying the conditions where robust learning occurs. It is based on observable conditions, activities and results. The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning. It is based on inference of unobservable conditions, activities and results.

	~~# For any instructional~~ design ~~issue~~, the ~~alternative designs can be rank~~ ordered ~~according to how much~~ assistance they offer the ~~learner~~.		Just as medicine finds indispensable both the clinical and biological levels of explanation, the PSLC has found both its levels to be indispensable as well. The macro level is like the clinical level—it is a relatively atheoretical classification of treatments and effects. The micro level is like the biological level—it explains why certain treatments have certain effects.
	~~# Learners can also~~ be ~~rank ordered~~ by how ~~far along they are in their acquisition~~ of ~~the~~ knowledge ~~addressed by the instruction~~.
	~~# Learning is most efficient when~~ the ~~two ranks match inversely~~. (~~Efficiency is usually defined as learning gains divided by~~ instructional ~~time~~.)		Although many classification schemes may make sense for the macro level (PSLC’s cluster-subcluster hierarchy is one), this page presents a multidimensional one. That is, this classification scheme defines a set of design dimension, where each dimension has a set of alternative, non-numeric values. A point in this multidimensional space is simply a specification of a value for each dimension. Each point in the space corresponds to a generic type of instruction.
			[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]
	~~That is, for beginning learners, high~~ assistance ~~instruction~~ is ~~more efficient than low assistance instruction~~, ~~and for advanced learners, low assistance instruction~~ is ~~more efficient than high assistance instruction~~. ~~This hypothesis~~ is ~~familiar to teachers,~~ and it is ~~often associated~~ with ~~Vygotsky~~ and the ~~motto “model~~, ~~scaffold, fade~~.” ~~Woods, Bruner and Ross [ref] coined~~ the ~~term “scaffolding” for assistance that~~ is ~~eventually removed~~ (~~faded~~)~~, but the term “scaffolding” is often mystifying~~ to ~~those outside~~ the ~~Learning Sciences, so we prefer “assistance~~.”
			Whenever possible, the values of a dimension are ordered or partially ordered by the amount of assistance they offer students, where higher assistance values raise performance during training. For instance, along the feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback. This ordering allows testing the PSLC’s Assistance Hypothesis:

			* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.

			Experiments are used to map out the multidimensional design space. A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others. If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately. Prior knowledge is measured by pretesting or its LearnLab equivalent. Immediate and robust learning is assessed with the usual LearnLab measures. Such an experiment determines how two indepentent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning). Such an experiment provides a test of a specific application of the Assistance Hypothesis. With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.

			Note that such an understanding would be only a macro level one (like the clinical level in medicine). A micro level understanding (like the biological level) is related but different. An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.

			[[Instructional dimensions root\|Click here for a list of the dimensions currently being investigated.]]

Vanlehn at 15:48, 30 March 2007

2007-03-30T15:48:30Z

New page

=== Draft version of a framework for the Macro Level ===

The macro level framework is based on a three-part hypothesis.

# For any instructional design issue, the alternative designs can be rank ordered according to how much assistance they offer the learner.
# Learners can also be rank ordered by how far along they are in their acquisition of the knowledge addressed by the instruction.
# Learning is most efficient when the two ranks match inversely. (Efficiency is usually defined as learning gains divided by instructional time.)

That is, for beginning learners, high assistance instruction is more efficient than low assistance instruction, and for advanced learners, low assistance instruction is more efficient than high assistance instruction. This hypothesis is familiar to teachers, and it is often associated with Vygotsky and the motto “model, scaffold, fade.” Woods, Bruner and Ross [ref] coined the term “scaffolding” for assistance that is eventually removed (faded), but the term “scaffolding” is often mystifying to those outside the Learning Sciences, so we prefer “assistance.”