Connecting Cognitive Domains of Bloom’s Taxonomy and Robotics to Promote Learning in K-12 Environment
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Bibliographic record
Abstract
Abstract Connecting Cognitive Domains of Bloom’s Taxonomy and Robotics to Promote Learning in K-12 EnvironmentLearning as currently represented in our K-12 educational system doesn’t lend itself to effectiveclassroom environments that stimulate the growth of students’ cognitive domain. Instead, manycurrent classroom practices rely on rigid theory, disconnected facts, and computational recipes.Such an approach fails to relate to students’ everyday experiences in life outside the classroom.Consequently, when classroom instruction fails to connect theory/facts/procedures with students’conceptualization of ideas, it results in a loss of significance, i.e., the students can neither recallnor appreciate the significance of their classroom learning. Alternatively, the ability to recalltheory/facts/procedures and their significance allow students to apply ideas more effectively anddevelop higher-order thinking to synthesize new concepts. In Bloom’s taxonomy, learning incognitive domain is categorized from simple to complex behaviors. Specifically, knowledge,comprehension, application, analysis, synthesis, and evaluation are the behaviors that aretypically mastered sequentially due to the nature of their increasing difficulty. Bloom’s methodallows accurate measurement of students’ learning progression through each level of behavior.As behavior at each level is learned sequentially, with each new step in the chain building on itspredecessor, this approach allows the development of a deeper level of understanding andhigher-order thinking. Designing and conducting classroom activities that support the cognitivelearning domains of Bloom’s taxonomy can allow students to develop their fundamental andhigher-order skill sets. Unfortunately, the current educational system exposes students to onlysome but not all of the core cognitive learning categories of Bloom’s taxonomy.In this paper, three concrete illustrations will demonstrate integration of the entire cognitivelearning domain with robotics lessons. The example lessons will address typical educationalobjectives of K-12 science and math disciplines and strengthen students’ ability to learn thesubject material. Three lessons, based on Lego NXT robotics, will be used to transcend agegroups from elementary school to high school levels. For example, one lesson will use a mobilerobot with an ultrasonic sensor to navigate around obstacles. First, to allow students to developknowledge and ability to recall, verbal and visual connections will be drawn between the robot’sultrasonic sensor and a bat’s echolocation. Second, to develop their comprehension, the studentswill perform experiments to establish how the ultrasonic sensor interacts with various objects inthe environment and its effect on measurements. Third, to develop their cognitive domain ofapplication, the students will construct a robot that is capable of movement and uses theultrasonic sensor to interact with its environment. Having addressed the fundamental cognitivelearning domains, the robotics lesson will be used to address students’ higher-order cognitiveskills. First, to allow the development of analysis skills, students will conduct an experimentinvolving the measurement of reaction time and robot behavior when the ultrasonic sensor is setto several different distance thresholds. Second, to develop their synthesis skills, student willemploy the data collected from the previous step and make inferences about rebuilding theirrobot to optimize its abilities to maneuver around obstacles. Finally, to develop their evaluationskills, students will obtain qualitative data on their newly synthesized robot design to determinethe results of their decisions. Such an approach will guide students through the entire cycle ofcognitive domains of Bloom’s taxonomy to ensure that all levels of learning are captured. Thefull version of the paper will include classroom assessment of the aforementioned activities, inelementary, middle, and high school grades, and recommendations for future work.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it