| Summary: | Research has been showing that teachers’ assessment practices are seen more as a technique to grade students rather than a pedagogical process that aims to support in-depth and meaningful learning. In order to come up with detailed descriptions of teachers’ assessment practices, the literature has been calling for the need to develop research based on data collected in real classrooms. On the other hand, it has been acknowledged that there is a complex relationship between teachers’ and students’ perceptions on learning assessment and its practices. Research reported in this paper was guided by the following three main objectives: a) to describe, to analyze and to interpret classroom learning assessment practices; b) to compare teachers’ and students’ perceptions on assessment; and c) to find out relationships between qualitative (intensive) and quantitative (extensive) data. Qualitative, intensive, data were collected at four universities through in-depth interviews with eight Arts and Humanities teachers (two per university), 160 h of classroom observations (20 h per teacher), and interviews with eight groups of students (two groups per university). These data enabled one to produce a total of eight narratives on learning assessment issues that gave rise to a single meta-narrative. Quantitative, extensive, data were collected through a 45-Likert items questionnaire, 15 of which were directly related to learning assessment. A descriptive analysis of these data was performed and Homogeneity Analysis (HOMALS) was used in order to identify the variables that most differentiate teachers’ and students’ perceptions on assessment issues. Research results showed that teachers’ assessment practices ranged from those that were oriented to support learning and to those whose only purpose was to grade students’ learning. Homogeneity analysis (HOMALS) allowed one to identify those variables that best discriminate teachers’ and students’ perceptions. Moreover, it contributed to establish relationships between extensive and intensive data.
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