人工智能在科学教学中的应用:以非洲科学教师为视角

IF 6.7 Q1 EDUCATION & EDUCATIONAL RESEARCH
Cecilia Obi Nja, Kimson Joseph Idiege, Uduak Edet Uwe, Anne Ndidi Meremikwu, Esther Etop Ekon, Costly Manyo Erim, Julius Ukah Ukah, Eneyo Okon Eyo, Mary Ideba Anari, Bernedette Umalili Cornelius-Ukpepi
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引用次数: 1

摘要

摘要本研究采用“技术接受模型”(Technology Acceptance Model, TAM)对科学教师使用“人工智能”(AI)的影响因素进行了调查。与TAM变量一起调查的因素包括教师数据;年龄、性别和居住类型。本研究中相关的TAM项目包括;自尊、压力和焦虑、易用性、行为意图、对人工智能使用的态度和预期收益。本研究的人口包括尼日利亚克罗斯河州卡拉巴尔教育区的所有科学教师(170人)。样本由79名科学教师组成,其中女性46人(58.22%),男性33人(41.77%)。本研究采用描述性和分析性研究设计。本研究采用了一份名为“对人工智能的认可:教师之眼”问卷(AAITEQ)。这项研究提出了3个研究问题。采用Cronbach’s alpha, aiteq的信度为0.72 ~ 0.81。调查结果表明,人工智能应用的认可度较高,总体平均得分为3.00分。使用TAM变量的行为意图的最高预测值是易用性r = .789。理科教师的性别(t, 77 = 1.988;p = 0.060, (p < 0.05),年龄F (2,76) = .547;P = .581 (P = .05)、教师居住地(t, .77 = .533;p =−0.062 (p < 0.05)不影响科学教师使用人工智能的意向行为。建议对在职和职前教师进行人工智能利用方面的培训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers
Abstract This study investigated the factors influencing science teachers' 'Artificial Intelligence' (AI) utilization by using the 'Technology Acceptance Model' (TAM). The factors investigated alongside TAM variables were teachers' data like; age, sex, and residence type. TAM items that were correlated in this study included; self-esteem, stress and anxiousness, ease of utilization, behavioural intention, attitude towards AI usage, and expected benefits. The population of this study comprised all science teachers (170) in the Calabar Education Zone of Cross River State, Nigeria. The sample was made up of 79 science teachers comprising (58.22%) 46 females and (41.77%) 33 males. The descriptive and analytical research design was used in this study. A questionnaire named ' Approval of Artificial Intelligence: The Teachers' Eye' Questionnaire (AAITEQ) was used for the study. This study raised 3 research questions. The reliability for AAITEQ was from 0.72 to 0.81 using Cronbach's alpha. Findings indicated that the approval for the utilization of AI was high with an overall mean score of 3.00. The highest predicting value for behaviour intent using TAM variables was the ease of usage r = .789. Science teachers' sex (t, 77 = 1.988; p = .060, ( p ˃ .05), age F (2, 76) = .547; p = .581 ( p ˃ .05) and teachers' residence location (t, .77 = .533; p = − .062 ( p ˃ .05) did not influence the behaviour of science teachers' intention of the utilization of AI. It was recommended that both in-service and pre-service teachers be trained on the utilization of AI.
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来源期刊
Smart Learning Environments
Smart Learning Environments Social Sciences-Education
CiteScore
13.20
自引率
2.10%
发文量
29
审稿时长
19 weeks
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