  Online coding environments can help support computing students gain
programming practice at their own pace. Especially informative feedback can be
beneficial during such self-guided, independent study phases. This research
aims at the identification of feedback types applied by CodingBat, Scratch and
Blockly. Tutoring feedback as coined by Susanne Narciss along with the
specification of subtypes by Keuning, Jeuring and Heeren constitute the
theoretical basis. Accordingly, the five categories of elaborated feedback
(knowledge about task requirements, knowledge about concepts, knowledge about
mistakes, knowledge about how to proceed, and knowledge about meta-cognition)
and their subtypes were utilized for the analysis of available feedback
options. The study revealed difficulties in identifying clear-cut boundaries
between feedback types, as the offered feedback usually integrates more than
one type or subtype. Moreover, currently defined feedback types do not
rigorously distinguish individualized and generic feedback. The lack of
granularity is also evident in the absence of subtypes relating to the
knowledge type of the task. The analysis thus has implications for the future
design and investigation of applied tutoring feedback. It encourages future
research on feedback types and their implementation in the context of
programming exercises to define feedback types that match the demands of novice
programmers.
