DOI: 10.57760/sciencedb.27965
title: Research on the Innovation of Teaching Evaluation and Feedback Mechanism for Food Specialization Based on Generative Artificial Intelligence
title: 生成式人工智能驱动的食品专业教学评估与反馈机制创新研究

description: This dataset focuses on the research on the innovation of assessment and feedback mechanisms for food professional teaching using generative artificial intelligence (GenAI). It consists of two sub-datasets, collecting relevant information from both the teacher and student perspectives, providing rich data support for in-depth exploration of the application effect and influence of GenAI in food professional teaching.Teacher perspective dataset ('Generation-Driven Innovation of Assessment and Feedback Mechanisms in Food Professional Teaching.xlsx')1. Data size: Contains 31 records, covering 32 different dimensions of relevant information.2. Data content• Basic information: Records the submission time of the answer sheet, the time spent, and the source, which can be used to understand the process and channels of data collection.• GenAI application situation: Involves the proportion of class hours where teachers use GenAI for assessment, as well as the scenarios where teachers consider GenAI assessment to be the most effective, such as theoretical assignments, laboratory reports, product design, and classroom interactions.• Teaching effect feedback: Includes the changes in the average scores of students in teaching links such as theoretical exams, laboratory reports, and product design after the introduction of GenAI assessment, as well as the reduction in grading time and the timeliness of feedback after using GenAI for automatic feedback.• Problems and solutions: Records the biggest conflicts encountered, whether GenAI assignment abuse was found, and the most effective identification methods (such as questioning details during the defense, on-site review of experimental operations, AI detection tools, etc.). It also includes content that needs to be publicly disclosed to improve assessment reliability, such as the source of AI model training data, the proportion of manual review of AI results, etc.• Teaching improvement direction: Involves teachers' views on GenAI in improving students' grades/capability, saving time costs, and cultivating practical innovation abilities, as well as evaluations of GenAI in real-time capturing experimental operation scores, automatically associating the latest food industry national standards, multimodal feedback, and academic compliance detection functions.Student perspective dataset ('Generation-Driven Innovation of Assessment and Feedback Mechanisms in Food Professional Teaching Student Version.xlsx')1. Data size: Contains 136 records, involving 29 related variables.2. Data content• Basic information: Similarly records the submission time of the answer sheet, the time spent, and the source, providing background information for data analysis.• Learning experience and comparison: Students' sources of understanding previous levels, and whether they believe they have performed better than previous students who did not use GenAI in terms of theoretical knowledge mastery, food laboratory operation standardization, and application of industry standards.• GenAI feedback impact: Includes the impact of personalized GenAI feedback on adjusting the frequency of learning focus, enhancing learning interest, and modifying homework/reports, as well as the final score changes. It also involves changes in task completion time (such as laboratory report writing, industry plan design) due to GenAI feedback.• Problem feedback: Whether students encountered difficulties in understanding the GenAI feedback and can describe specific cases. At the same time, students' views on the content that must be disclosed to trust GenAI assessment (such as comparison of previous and current score standards, statistics of AI scoring errors, appeal and review process, etc.).• Expectation function evaluation: Students' expectations for GenAI in improving grades/capability, saving time costs, and cultivating practical innovation abilities, as well as evaluations of GenAI in real-time capturing experimental operation scores, automatically associating the latest food industry national standards, multimodal feedback, and academic compliance detection functions.
description: This dataset focuses on the research on the innovation of assessment and feedback mechanisms for food professional teaching using generative artificial intelligence (GenAI). It consists of two sub-datasets, collecting relevant information from both the teacher and student perspectives, providing rich data support for in-depth exploration of the application effect and influence of GenAI in food professional teaching.Teacher perspective dataset ('Generation-Driven Innovation of Assessment and Feedback Mechanisms in Food Professional Teaching.xlsx')1. Data size: Contains 31 records, covering 32 different dimensions of relevant information.2. Data content• Basic information: Records the submission time of the answer sheet, the time spent, and the source, which can be used to understand the process and channels of data collection.• GenAI application situation: Involves the proportion of class hours where teachers use GenAI for assessment, as well as the scenarios where teachers consider GenAI assessment to be the most effective, such as theoretical assignments, laboratory reports, product design, and classroom interactions.• Teaching effect feedback: Includes the changes in the average scores of students in teaching links such as theoretical exams, laboratory reports, and product design after the introduction of GenAI assessment, as well as the reduction in grading time and the timeliness of feedback after using GenAI for automatic feedback.• Problems and solutions: Records the biggest conflicts encountered, whether GenAI assignment abuse was found, and the most effective identification methods (such as questioning details during the defense, on-site review of experimental operations, AI detection tools, etc.). It also includes content that needs to be publicly disclosed to improve assessment reliability, such as the source of AI model training data, the proportion of manual review of AI results, etc.• Teaching improvement direction: Involves teachers' views on GenAI in improving students' grades/capability, saving time costs, and cultivating practical innovation abilities, as well as evaluations of GenAI in real-time capturing experimental operation scores, automatically associating the latest food industry national standards, multimodal feedback, and academic compliance detection functions.Student perspective dataset ('Generation-Driven Innovation of Assessment and Feedback Mechanisms in Food Professional Teaching Student Version.xlsx')1. Data size: Contains 136 records, involving 29 related variables.2. Data content• Basic information: Similarly records the submission time of the answer sheet, the time spent, and the source, providing background information for data analysis.• Learning experience and comparison: Students' sources of understanding previous levels, and whether they believe they have performed better than previous students who did not use GenAI in terms of theoretical knowledge mastery, food laboratory operation standardization, and application of industry standards.• GenAI feedback impact: Includes the impact of personalized GenAI feedback on adjusting the frequency of learning focus, enhancing learning interest, and modifying homework/reports, as well as the final score changes. It also involves changes in task completion time (such as laboratory report writing, industry plan design) due to GenAI feedback.• Problem feedback: Whether students encountered difficulties in understanding the GenAI feedback and can describe specific cases. At the same time, students' views on the content that must be disclosed to trust GenAI assessment (such as comparison of previous and current score standards, statistics of AI scoring errors, appeal and review process, etc.).• Expectation function evaluation: Students' expectations for GenAI in improving grades/capability, saving time costs, and cultivating practical innovation abilities, as well as evaluations of GenAI in real-time capturing experimental operation scores, automatically associating the latest food industry national standards, multimodal feedback, and academic compliance detection functions.