In this paper, we present a standard-based methodology to data-mine and eventually compute the results from 360 • feedback surveys. We pay special attention to its suitability in the educational and pedagogical sphere, where very little research efforts have been accomplished. A 360 • feedback study (also known as multi-rater feedback) provides a holistic view of an appraisee's performance based on the information and opinions gathered from raters about the person being evaluated. An evaluator or appraiser can be any peer, coworker, subordinate, supervisor, customer, classmate, and so on and even the appraisee himself/herself. This type of surveys is usually carried out in big companies/institutions and by specialized human resources departments. The computer tools normally used for the processing of the data generated by these studies are based on simple spreadsheets and ad hoc scripts backed by untapped databases. In this context, we advocate the use of a refined, standardized, and straightforward computing environment based on stored procedures. The Persistent Stored Module and the stored procedures there defined are part of the Structured Query Language ISO norm, and they can be leveraged to perform complex calculations without the need of developing external software or requiring extra network transactions. This approach can also be transferred to general 360 • appraising studies and, with special emphasis, to those performed in academic levels. As a testbed, the presented techniques have been put into practice in a multi-rater evaluation experience executed with over 240 high-school students. The technical and pedagogical lessons learned in this initial test can be eventually exported to the three main academic levels in a network of educational centers with more than 44k scholars enrolled. The inclusion of such assessment actions in educational environments enables the fostering of the novel pattern of cooperative learning, as it is also highlighted in this paper. INDEX TERMS 360• feedback, SQL/PSM, stored procedures, data-mining, cooperative learning.

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