Understanding the Role of AI and Learning Analytics Techniques in Addressing Task Difficulties in STEM Education

Utility of video analysis and expert modelling for technique development in novice sport climbers: A randomized controlled study

“…While our study was not designed to establish a causal effect of the task difficulty on the outcome, there are some interesting clues about this in our data (albeit only on the level of association). Namely, the results of our study suggest that for the high step and drop knee, there is a negative association between the perceived difficulty and the outcome, which is in line with previously discussed findings of Nawaz 38 and Akizuki 44 et al For the heel hook, we only observed this association during pre testing but not post intervention. These results are important since they suggest that task difficulty could be a potential mediator for the drop knee and high step but not so for the heel hook.…”

“…Although the two represent different constructs they are often mistakenly used interchangeably. 38,39 Task complexity represents the characteristics or cognitive demands of a task, while task difficulty refers to a person's subjective judgment of the task complexity. 39 Note that in the causal inference setup under the randomized controlled experiment, these two constructs play very distinct roles: while both can affect the effectiveness of the treatment as will be argued later, only task difficulty can be a mediator in the underlying causal mechanism.…”

“…The relationship between task difficulty and the outcome of the learning process has only recently been established in the literature. 38 In particular, Nawaz and colleagues 38 showed that if students perceive the task difficulty as easy or hard , it may lead to poorer learning outcomes, while medium or moderate task difficulty may result in better learning outcomes. The U-shaped relationship between motor learning and task difficulty has also been observed 44 indicating that too challenging and/or too easy tasks impair motor learning trajectory.…”

Curriculum Analytics of Course Choices:

“…Additionally, the concept of course difficulty as presented in this study may be combined with other difficulty metrics, such as fail rate and average grade, to gain further insights. The aim of this study, however, is not to suggest that all course content should be made easier; rather, it is to suggest that courses be designed to optimize the difficulties for individual students, i.e., within the optimal zone for students, where they struggle a bit, but not so much that they fail or disengage (Nawaz, Alghamdi, et al, 2022;Vygotsky, 2012).…”

“…In higher education, students are expected to take charge of their learning by deciding "what" to learn and "how" to learn (OECD, 2018). While the learning analytics (LA) community has seen increasing research on the "how" to learn part, where e.g., it is investigated how to support students in their learning journey by promoting their engagement (D'Mello et al, 2010), facilitating their meta-cognitive awareness (Matcha et al, 2020), designing feedback to promote positive affective experiences (Berland et al, 2014;, and designing the learning environments to be increasingly interactive (e.g., through the use of simulation-based (Nawaz, Srivastava, Yu, et al, 2020 and virtual reality-based (Nawaz, Alghamdi, et al, 2022) learning designs). The "what" to learn part (Dennehy et al, 2021), which mainly deals with students' curriculum choices, such as making a choice about which courses they want to take, is still under investigation.…”