The presumption to the given research question (RQ1) is that educators are only able to use the full potential of intelligent tutoring systems if they (1) have access to the information obtained from the learners, (2) are able to understand and interpret this information, (3) and can transform this interpretation into valuable pedagogical and didactical actions. This aligns with the learning analytics process model [31] (see Figure 2), which is applicable to learner or teaching dashboards and authoring interfaces.
Hence, regarding the results from an ongoing systematic literature review, we concluded a revised meta-architecture of intelligent tutoring systems that incorporate the role of educators (Figure 1 black elements). This draws attention to the design of teaching dashboards, allowing views into and interactions with the different models of such systems (compare with Figure 1). Besides static dashboards, a functional entity is needed to support the educators´ reflection process about the effectiveness of their teaching methods. We call this entity educator model.
With the given summary, the illustrated current systematic overview (Figure 1 gray elements), and a visualized extension proposal (Figure 1 black elements), we would like to point out a major implication for teachers in higher education when introducing intelligent tutoring systems into their educational setting.
If teachers in higher education are using or will start using intelligent tutoring systems, they should reflect on three main questions.
(1) Do I have access to all information incorporated into the different models of intelligent tutoring systems?
(3) I am able to transform this interpretation into pedagogical or didactical actions?
With this contribution, we hope to give higher education teachers some leverage to participate in the discussion of the design of intelligent tutoring systems.
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