د/ايمان زغلول قاسم

استاذ تكنولوجيا التعليم المشارك بكلية التربية بالزلفي

book B58

operationalisation of time spent examining the feedback. Using eye-tracking technologies
could reveal more detailed images of student behaviour in examining feedback in computerbased
environments.
Furthermore, the effects reported in this meta-analysis were all short-term effects,
measured using a post-test that was administered either immediately or shortly after the
feedback intervention. For feedback to realise its full formative potential, however,
continuous feedback loops are needed throughout the entire learning process.
The literature on the effectiveness of feedback suggests that the complex relationships
between the feedback intervention, the task, the learning context, and the characteristics of the
learner impact the magnitude of feedback effects (Shute, 2008). However, the primary studies
that have been published to date have reported insufficient data to meaningfully examine
these complex relationships. For example, research suggests that the initial ability levels of
the learner affect feedback effectiveness (e.g., Hattie & Gan, 2011; Smits, Boon, Sluijsmans,
& Van Gog, 2008), but only a small number of studies in this meta-analysis reported
information about the initial ability of the learners. Hattie and Gan have suggested that
feedback needs to be appropriate for the level at which the learner is functioning. In a
computer-based environment, the mechanisms of computerised adaptive testing (CAT;
Wainer, 2000) are very promising in this respect. If not only the item selection but also the
selection of the specific feedback to the item response could be selected based on the current
ability of the learner, feedback can perhaps fulfil its formative potential to a greater extent
(Veldkamp, Matteucci, & Eggen, 2011). In addition, providing feedback that is gradually
becoming more elaborated in an interactive manner could be used to adapt the feedback to the
needs of learners. This kind of feedback is called intelligent tutoring feedback (ITF; Narciss,
2008). Digital learning environments could become even more powerful when the feedback
includes a diagnostic component, meaning that it has been designed to address, prevent, or
correct misconceptions or frequently made errors. However, research is needed to explore the
potentials of computer-based adaptive learning environments and adaptive feedback
mechanisms.
Moreover, the degree in which the psychometrical properties of the instruments used
have been reported in the primary studies is strikingly low. For example, only a few studies
reported the reliability (α) of the post-test. Furthermore, the number of items used in the posttest
was also not reported in all studies, and the studies that did report the number of items
used a strikingly low number. These assessments are insufficiently reliable for making wellgrounded
claims about the differences in the effects of various feedback types. Also, only a
few studies reported the difficulty level of the items in the assessments. In future research, it
is recommended that these psychometrical properties be reported because they can be used to
more accurately weight the effect sizes. These data could also be used to examine the
relationships between optimal item difficulty in formative computer-based assessments and
the initial ability of learners since difficult items leave room for more opportunity to learn
from feedback than easier items (Van der Kleij et al., 2011). Ultimately, these insights could
be used to optimise computer adaptive learning environments (e.g., Wauters, Desmet, & Van
den Noortgate, 2010). Finally, in future research, it is recommended that larger groups of
participants be used.
Chapter 4
86

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