Abstract:

Motivation:
Many calls for integrated STEM emphasize the need to engage students with complex tasks solving. With maturing the field, those educational systems tend to become more complex in structure, functionality (tasks) and cognitive load for learners. The STEM task complexity is important due to multiple reasons.
(1) The nature of STEM research and practice rely on complex real-world tasks solving.
(2) Within some educational environment (EE), task solving covers multiple activities, and guided by learners, pushes the whole learning process.
(3) Task solving process is, in fact, the interaction among a variety of components within an EE.
(4) Understanding the essence and nature of task complexity is valuable for both students and teachers in guiding and managing the process.

Basic idea & approach:
Task complexity is a global problem and important for both non-STEM and STEM fields. So far, we yet little know what the term “task complexity”, in essence, is in various STEM contexts. In this paper, we consider the task complexity in the context of STEM-driven Computer Science education with a conceptual framework proposed to analyse and evaluate the task complexity issues. To build this framework, we have thoroughly studied the complexity contributing factors, objective and subjective complexity measures, and various task complexity models from the literature. We have adapted all these in our context. Next, we have selected TPACK framework as a basis to develop our conceptual framework.

We put at the centre STEM-driven task model, which includes:
(i) external inputs (learner’s model, etc.)
(ii) internal inputs (Technology (T), Pedagogy (P) and Content (C) components’ models). The next constituents are
(iii) processing and
(iv) set of outputs (task solving scenario, processes and Integrated STEM skills as a final product). All constituents have a variety of complexity contributing factors (also characteristics, features) along with adequate objective/subjective measures. We introduce a five-level Likert scale to evaluate the relationship among task constituents and task complexity objective/subjective fuzzy measures.

Methodology & results:
Our methodology relies on:
(1) developing the framework constituents’ models,
(2) STEM-driven task complexity structural model and
(3) modelling the interaction to define the relationship among task complexity contributory factors and objective/subjective task complexity measures.

We present models using the feature-based notion. This representation is an abstract pure structural representation without the constraint relationships. Analysing the models and relying on the human expert knowledge, we can define the anticipated relationships and evaluate their complexity using a five-level Likert scale (1- lowest, 5- highest complexity level). To validate our approach, we present a case study with the task “A set of LEDs” complexity analysis.

Concluding remarks & limitations:
Task complexity is a multifaceted problem dependable on the task type, context of use and learner. The proposed framework present a general understanding of the task complexity problem in the context of STEM education. This understanding enables a better guiding and managing the processes for seeking a higher performance in gaining a variety of STEM skills. Limitations include restricted processing, no EE details and a restricted number of measures.

Keywords:

Task complexity, STEM, task complexity models, complexity measures.