Measuring the technological pedagogical content knowledge (TPACK) of in-service teachers of computer science who teach algorithms and programming in upper secondary education

• URL: http://arxiv.org/abs/2105.09252v1
• Date: Mon, 17 May 2021 17:54:19 GMT
• Title: Measuring the technological pedagogical content knowledge (TPACK) of in-service teachers of computer science who teach algorithms and programming in upper secondary education
• Authors: Spyridon Doukakis, Alexandra Psaltidou, Athena Stavraki, Nikos Adamopoulos, Panagiotis Tsiotakis, Stathis Stergou
• Abstract summary: This study examines a national sample of 1032 secondary teachers of computer science. It measures their knowledge with respect to technology, pedagogy, content knowledge and the combination of each of these areas.
• Score: 55.41644538483948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Based on the Technological Pedagogical and Content Knowledge (TPACK) framework (Mishra & Koehler, 2006) and the Schmidt et al. (2009) instrument which explore TPACK, this study examines a national sample of 1032 secondary teachers of computer science and measures their knowledge with respect to technology, pedagogy, content knowledge and the combination of each of these areas. Findings indicate that content knowledge and technology knowledge rating are high (average 4.38 and 4.16 respectively) and it seems that secondary teachers are less confident with their pedagogical content knowledge and their technological content knowledge (average 3.51 and 3.68 respectively).

Related papers

• Teaching Quantum Informatics at School: Computer Science Principles and Standards [0.0]
  Quantum informatics is relevant to computer science education, but little research has been done on how to teach it. In this study, we position quantum informatics within Denning's Great Principles of Computing and propose Quantum Informatics Standards for secondary schools.
  arXiv  Detail & Related papers  (2024-07-17T06:32:37Z)
• Representing Pedagogic Content Knowledge Through Rough Sets [0.0]
  The paper is meant for rough set researchers intending to build logical models or develop meaning-aware AI-software to aid teachers. The main advantage of the proposed approach is in its ability to coherently handle vagueness, multi-modality.
  arXiv  Detail & Related papers  (2024-02-26T11:00:45Z)
• Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators [78.63553017938911]
  Large language models (LLMs) outperform information retrieval techniques for downstream knowledge-intensive tasks. However, community concerns abound regarding the factuality and potential implications of using this uncensored knowledge. We introduce CONNER, designed to evaluate generated knowledge from six important perspectives.
  arXiv  Detail & Related papers  (2023-10-11T08:22:37Z)
• Level of Awareness of PSU Bayambang Campus Students towards E learning Technologies [0.0]
  The study assesses the awareness of PSU Bayambang Campus students regarding e-learning technologies. Around 52.50% of respondents were familiar with e learning concepts, but their exposure and utilization levels need consideration. Technology, Support, and Users were identified as key factors influencing student awareness.
  arXiv  Detail & Related papers  (2023-08-06T13:54:39Z)
• Knowledge-augmented Deep Learning and Its Applications: A Survey [60.221292040710885]
  knowledge-augmented deep learning (KADL) aims to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning.
  arXiv  Detail & Related papers  (2022-11-30T03:44:15Z)
• Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution [82.89021683451432]
  We propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods.
  arXiv  Detail & Related papers  (2022-07-18T02:41:04Z)
• The use of Semantic Technologies in Computer Science Curriculum: A Systematic Review [0.0]
  This paper provides an overview of the application of semantic technologies in the context of the Computer Science curriculum. The alignment of and accurate curricula assessment appears to be the most significant limitations to the widespread adoption of such technologies.
  arXiv  Detail & Related papers  (2022-05-01T12:51:58Z)
• Understanding the role of single-board computers in engineering and computer science education: A systematic literature review [0.0]
  Single-Board Computers (SBCs) have been employed more frequently in engineering and computer science both to technical and educational levels. This systematic literature review explores how the SBCs are employed in engineering and computer science.
  arXiv  Detail & Related papers  (2022-03-30T18:34:03Z)
• A Survey of Knowledge-Enhanced Text Generation [81.24633231919137]
  The goal of text generation is to make machines express in human language. Various neural encoder-decoder models have been proposed to achieve the goal by learning to map input text to output text. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.
  arXiv  Detail & Related papers  (2020-10-09T06:46:46Z)
• A Survey on Knowledge Graphs: Representation, Acquisition and Applications [89.78089494738002]
  We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
  arXiv  Detail & Related papers  (2020-02-02T13:17:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.