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In general terms, the aims of formative assessment are to gather and analyze information about the progress of students, with the intention of improving instruction in real time. This can also help teachers to identify those elements that students are struggling with, and therefore to determine where adjustments must be made. There are, however, some issues that must be addressed before this approach can become more widespread. In this research, and with mathematics as our specific scope, we focus on two of these issues: we present a framework to help teachers implement assessment items that are highly dynamic and flexible, but that allows them at the same time to model common misconceptions and to provide suitable feedback. Following this, we carried out an experiment to determine (i) whether the students perceived such feedback to be useful; (ii) whether they actually used it; and ultimately (iii) whether it produced a difference in their performance. To find the answers to these questions, we used an online `pre-calculus' course with 458 students and collected data directly from those students via both a perception survey and from their interaction with the LMS. Based on these data, we performed several analyses, including process mining and statistical hypothesis tests. The results demonstrated that although many of the students had a positive opinion of the feedback received, it was not always followed. Nevertheless, when it was (or at least when students said it was), the students performed better.

Traditionally, the assessment of upper limb (UL) motor function in neurorehabilitation is carried out by clinicians using standard clinical tests for objective evaluation, but which could be influenced by the clinician's subjectivity or expertise. The automation of such traditional outcome measures (tests) is an interesting and emerging field in neurorehabilitation. In this paper, a systematic review of systems focused on automation of traditional tests for assessment of UL motor function used in neurological rehabilitation is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the automation level, the data acquisition systems, the outcome generation method, and the focus of assessment. Finally, a series of technical requirements, guidelines, and challenges that must be considered when designing and implementing fully-automated systems for upper extremity functional assessment are summarized. This paper advocates the use of automated assessment systems (AAS) to build a rehabilitation framework that is more autonomous and objective.