The results of this study, namely: (1) the average student HOTS score was 67.79 including the "High" category; (2) mastery of HOTS skills from the total score of test questions: (a) analyzing 68.82% on the organizing thinking process. (b) evaluating 61.51% on the examining ...
to assess higher- order thinking skills in didactic curricula of large classes can be onerous. Quite often, instructors use multiple-choice questions (MCQs). However, creating higher- order thinking questions in MCQ format is very challenging and time-consuming because typically a scenario, ...
Over the past two decades, the field of STEM education has produced a wealth of research findings. This study systematically reviewed the published literature from the perspective of subject integration and theme evolution, considering both K-12 and high
Online peer feedback is an effective instructional strategy to enhance students' learning processes and outcomes. However, the literature lacks a compr
Towards the end Fister notes a focus by higher education institutions on metrics to justify the cost of academic programs and raises questions for libraries on whether they place enough emphasis on democracy, public good and social responsibility in such an environment. Also focused on the US is ...
The sample consisted ... LE Richland,N Simms 被引量: 0发表: 0年 The Fundamental Skills of Higher Order Thinking It may be possible to teach reasoning strategies to subjects with poor reasoning, including many subjects with learning disabilities (LD), using curriculum... B Grossen - 《J ...
high-level tasks to give our students. Instead, we often default to having students identify and define terms, label things, or answer basic recall questions. It’s what we know. And we have so much content to cover, many of us might feel that there really isn’t time for the higher-...
During the COVID-19 pandemic, the flipped classroom (FC) approach has been a prominent teaching and learning strategy. Despite its popularity, few studies have been undertaken to effectively measure student learning experiences in an FC learning environm
(NLP) methods, 3) feature engineering using the CountVectorizer method to convert questions into feature vectors, and 4) a classification module based on the Logistic Regression (LR) algorithm to categorize exam questions into categories like knowledge, comprehension, application, analysis, synthesis, ...
Each section was taught in a high-level inquiry based style but was assigned either low-level questions (memory oriented) on the quizzes and exams, or high-level questions (application, evaluation, and analysis) on the quizzes and exams for the entirety of the semester. A final exam ...