Leveraging Algorithmic Integration in Industrial Technology Education for Achieving a Skilled and Inclusive Society
Keywords:
Algorithm Integration, Decision Tree, Artificial Neural Networks, Generative Adversarial Networks, Skilled, Industrial TechnologyAbstract
This research determined participants’ opinions on leveraging algorithmic integration in Industrial Technology Education for achieving a skilled and inclusive society. The study area was technical colleges in Rivers State. A descriptive research design was utilized for the study. The target population included all (46) teachers in the four Technical Colleges owned by Rivers State. Due to the relatively small size of the population, census sampling was used to draw the entire population for this research. The study was designed to address three research questions. A
validated questionnaire consisting of 21 items was used to elicit information from respondents. Data were collected from participants through the administration of a four (4) point rating system of Very High Extent (VHE), High Extent (HE), Low Extent (LE), and Very Low Extent (VLE), assigned numerical values of 4, 3, 2, and 1, respectively. The researcher and three research assistants administered the instrument to participants at the same time and collected it from them after it had been completed. Data were analyzed using mean statistics. Items having a mean rating of 2.50 and above were considered Agree, while items below 2.50 were considered Disagree. Results revealed that integrating Decision Tree, Artificial Neural Networks, and Generative Adversarial Networks algorithms significantly contributes to the development of a skilled and inclusive society. Consequently, it was recommended, among other that the curriculum in Industrial Technology Education (ITE) be strategically redesigned to incorporate algorithmic integration.
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