Using online decision trees to support students’ self-efficacy in the laboratory

Abstract

Failed experiments are a common occurrence in research, yet many undergraduate science laboratories rely on established protocols to ensure students are able to obtain results. While it is logistically challenging to facilitate students’ conducting their own experiments in the laboratory, allowing students to “fail” in a safe environment could help with the development of problem-solving skills. To allow students a safe place to fail and encourage them to think through a laboratory protocol, online decision trees were created to lead students through protocols and give them timely feedback. The online decision trees present students with a scenario, then students execute a protocol by selecting options that will lead them down different paths and result in various realistic results from their experiments. They receive feedback and instructional tutorials throughout the simulation that are dependent on their choices. The significance of this new resource for student learning is that it allows students to practice their problem-solving skills and gain theoretical knowledge about the purpose of various experimental steps. The purpose of this research study was to evaluate whether online decision trees affected students’ self-efficacy, metacognition, and motivation for completing a wet laboratory. A mixed-methods approach was used; three surveys were administered throughout the academic term. For survey 1, students completed the decision tree and survey before the wet laboratory. For survey 2, students completed the survey before the wet laboratory but completed the decision tree after the wet laboratory. Students’ reported self-efficacy and intrinsic motivation were increased with the administration of the online decision trees before the wet laboratory, but their extrinsic motivation and metacognitive scores were unchanged. For survey 3, students provided written feedback about the impact of the online decision trees, and their responses highlighted the importance of the visual components of the approach.

Teaching students to use Decision trees (Dt) for unstructured data

“…The research (McLean et al, 2020) analyzed the use of online decision trees to support student self-efficacy in the research. The study assessed the effectiveness of online decision trees in metacognition, motivation, and self-efficacy.…”

“…The scholars recommend the CART and BRT models for analysis and understanding the missing data. The online decision trees were used to support the self-efficacy of students in the research laboratory (McLean et al, 2020). Researchers often report experimental failure; however, many laboratories rely on established protocols to ensure students can get trustworthy results.…”

Analyzing the Influence of College Aesthetic Education Teaching on College Students’ Innovation Ability and Artistic Literacy Based on Decision Tree Classification Model

“…Mclean et al proposed a costsensitive decision tree model, which quickly became a hot research topic in the related field. Nearly nine different types of cost-sensitive decision models including misclassification cost, testing cost, instructor cost, computational cost, intervention cost, unnecessary achievement cost, human-computer interaction cost, case cost, and instability cost were developed [25].…”