Purpose: This study explores the potential of the Farm Accountancy Data Network (FADN) to assess the technical, economic and environmental effects of cooperative innovation projects at farm level.
Design/Methodology/Approach: The analysis of the FADN potential relies on the classification of innovations and the co-identification, through the Most Significant Change (MSC) approach, of the most relevant indicators for tracking and assessing the performances of innovations and attributing to them the effects of a specific innovation.
Findings: The study shows that the FADN has a certain range of useful indicators and that they have a good coverage of the different types of innovation. Furthermore, the farm visits let emerge the importance of participatory approaches to capture the different changes and interplays occurred in farming processes.
Practical implications: The paper lays the foundations for the adaptation of the current methodology for data collecting and provides useful insights concerning the overcoming representativeness claims, costs’ issues, and problems related to the observation time limit.
Theoretical implications: The paper reveals the importance of participatory monitoring and evaluation approaches in helping the collection of more robust and relevant account data on farm, as well as in attributing certain results to a specific innovation and recognising synergies and side-effects of cooperative processes.
Originality/Values: The paper provides some recommendations on how to enlarge the scope of the FADN survey in order to be used effectively in the analysis of the performance of cooperative innovations at farm level. 相似文献
Traditional item analyses such as classical test theory (CTT) use exam-taker responses to assessment items to approximate their difficulty and discrimination. The increased adoption by educational institutions of electronic assessment platforms (EAPs) provides new avenues for assessment analytics by capturing detailed logs of an exam-taker's journey through their exam. This paper explores how logs created by EAPs can be employed alongside exam-taker responses and CTT to gain deeper insights into exam items. In particular, we propose an approach for deriving features from exam logs for approximating item difficulty and discrimination based on exam-taker behaviour during an exam. Items for which difficulty and discrimination differ significantly between CTT analysis and our approach are flagged through outlier detection for independent academic review. We demonstrate our approach by analysing de-identified exam logs and responses to assessment items of 463 medical students enrolled in a first-year biomedical sciences course. The analysis shows that the number of times an exam-taker visits an item before selecting a final response is a strong indicator of an item's difficulty and discrimination. Scrutiny by the course instructor of the seven items identified as outliers suggests our log-based analysis can provide insights beyond what is captured by traditional item analyses.
Practitioner notes
What is already known about this topic
Traditional item analysis is based on exam-taker responses to the items using mathematical and statistical models from classical test theory (CTT). The difficulty and discrimination indices thus calculated can be used to determine the effectiveness of each item and consequently the reliability of the entire exam.
What this paper adds
Data extracted from exam logs can be used to identify exam-taker behaviours which complement classical test theory in approximating the difficulty and discrimination of an item and identifying items that may require instructor review.
Implications for practice and/or policy
Identifying the behaviours of successful exam-takers may allow us to develop effective exam-taking strategies and personal recommendations for students.
Analysing exam logs may also provide an additional tool for identifying struggling students and items in need of revision.
