Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.

Productive Failure (PF) facilitates students’ conceptual knowledge by delaying instruction until after problem solving. While PF is well investigated in middle and high school students, little is known about its effectiveness in younger students. Studies in younger samples, which implemented delayed instruction designs similar to those used in PF studies, showed mixed results. However, these studies did not implement two core design components of PF: (1) contrasting and comparing student-generated solutions and the canonical solution during the instructional phase (contrasting activity), and (2) students’ collaboration in small groups during the initial problem-solving phase. Both components can be expected to contribute to the effectiveness of PF. In a quasi-experimental study with 228 fifth graders, we implemented the first component (contrasting activity) with all students to establish whether under this condition, problem solving prior to instruction would be more effective for younger students’ conceptual knowledge acquisition than direct instruction (i.e., problem solving after instruction). Further, we experimentally tested the effect of the second component (collaborative vs. individual problem solving) on students’ conceptual knowledge and the number of solution ideas generated during initial problem solving. We found no empirical support for either of our hypotheses. To explore the extent to which students’ collaboration actually achieved its potential and relates to students’ conceptual knowledge and solution ideas in PF, we conducted analyses of collaborative processes. Our study adds to the mixed results regarding the superiority of problem solving prior to instruction for young students, thus opening the discussion about age-related prerequisites as boundary conditions for PF.

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We conducted a user study that explored the relationship between students' usage of multiple external representations and their affective states during fractions learning. We use the affective states of the student as a proxy indicator for the ease of reasoning with the representation. Extending existing literature that highlights the advantages of learning with multiple external representations, our results indicate that low-performing students have difficulties in reasoning with representations that do not fully accommodate the fraction as a part-whole concept. In contrast, high-performing students were at ease with a range of representations, including the ones that vaguely involved the fraction as part-whole concept.

This paper investigates the effect of affect-aware support on learning tasks that differ in their cognitive demands. We conducted a study with the iTalk2learn platform where students are undertaking fractions tasks of varying difficulty and assigned in one of two groups; one group used the iTalk2learn platform that included the affect-aware support, whereas in the other group the affect-aware support was switched off and support was provided based on students' performance only. We investigated the hypothesis that affect-aware support has a more pronounced effect when the cognitive demands of the tasks are higher. The results suggest that students that undertook the more challenging tasks were significantly more inflow and less confused in the group where affect-aware support was provided than students who were supported based on their performance only.