Difference between revisions of "Root node"
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=== Abstract === | === Abstract === | ||
| − | PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]]. | + | PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be [[robust learning|robust]]. Although normal learning can be measured with immediate, near-transfer post-tests, we measure [[robust learning|robustness]] with three addition measures: [[long-term retention]], far [[transfer]] and [[accelerated future learning]]. |
=== Glossary === | === Glossary === | ||
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=== Dependent variables === | === Dependent variables === | ||
| − | Measures of basic learning (an immediate, near-transfer post-test) and measures of [[robust learning]] (long-term retention, | + | Measures of basic learning (an immediate, near-transfer or [[normal post-test|"normal" post-test]]) and measures of [[robust learning]] ([[long-term retention]], [[transfer]] and [[accelerated future learning]]). These measures typically appear as problems, activities, or items on paper and/or on-line tests. Measures are designed to assess whether students have acquired particular [[knowledge components]] at the right level of generality (see [[feature validity]]) and with sufficient [[strength]] to be retained for future use. |
=== Independent variables === | === Independent variables === | ||
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At the [[macro level]], instruction produces robust learning if it increases the frequency of: | At the [[macro level]], instruction produces robust learning if it increases the frequency of: | ||
| − | *[[sense making]] processes: rederivation, adaptation and self-supervised learning | + | *[[sense making]] processes: rederivation, adaptation (see [[refinement]], the explicit kind) and [[self-supervised learning]] |
| − | *and [[foundational skill building]] processes: strengthening, deep feature perception and [[cognitive headroom]]. | + | *and [[foundational skill building]] processes: [[strength|strengthening]], deep feature (see [[refinement]], the implicit kind)perception and [[cognitive headroom]]. |
At the [[micro level]], instruction produces robust learning if: | At the [[micro level]], instruction produces robust learning if: | ||
| − | *The instruction is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid. | + | *The instruction is designed so that the [[learning event space]] has some target paths that would cause an ideal student to acquire knowledge that is deep, general, [[strength|strong]] and [[feature validity|retrieval-feature-valid]]. |
*Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction. | *Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction. | ||
Latest revision as of 15:05, 13 October 2009
Contents
PSLC theoretical hierarchy’s Root Node
Abstract
PSLC research is primarily concerned with finding out what instructional environments, methods or activities causes students’ learning to be robust. Although normal learning can be measured with immediate, near-transfer post-tests, we measure robustness with three addition measures: long-term retention, far transfer and accelerated future learning.
Glossary
Research question
What instructional activities or methods cause students’ learning to be robust?
How can science and technology improve academic learning and make such learning more robust?
Dependent variables
Measures of basic learning (an immediate, near-transfer or "normal" post-test) and measures of robust learning (long-term retention, transfer and accelerated future learning). These measures typically appear as problems, activities, or items on paper and/or on-line tests. Measures are designed to assess whether students have acquired particular knowledge components at the right level of generality (see feature validity) and with sufficient strength to be retained for future use.
Independent variables
Independent variables in PSLC are primarily instructional activities, methods, or treatments. Studies might also include independent variables that measure individual differences, like a language students' first language.
Hypotheses
Learning will be robust if the instructional activities are designed to include appropriate paths, and the students tend to follow those paths during instruction.
Explanation
Instructional activities influence the depth and generality of the students’ acquired knowledge components, the knowledge components’ strength and feature validity, and the student’s motivation. These in turn influence the students’ performance on measures of robust learning. That is, we take a cognitive stance, rather than a radically distributed or situated stance.
At the macro level, instruction produces robust learning if it increases the frequency of:
- sense making processes: rederivation, adaptation (see refinement, the explicit kind) and self-supervised learning
- and foundational skill building processes: strengthening, deep feature (see refinement, the implicit kind)perception and cognitive headroom.
At the micro level, instruction produces robust learning if:
- The instruction is designed so that the learning event space has some target paths that would cause an ideal student to acquire knowledge that is deep, general, strong and retrieval-feature-valid.
- Most students follow a target path most of the time. There are many factors outside the easy control of the experimenter or instructor, such as motivation and recall, that affect whether students actually follow the target paths designed into the instruction.
