Plan-based delivery composition in intelligent tutoring systems for introductory computer programming

In a shell system for the generation of intelligent tutoring systems, the instructional model that one applies should be variable independent of the content of instruction. In this article, a taxonomy of content elements is presented in order to define a relatively content-independent instructional planner for introductory programming ITS's; the taxonomy is based on the concepts of programming goals and programming plans. Deliveries may be composed by the instantiation of delivery templates with the content elements. Examples from two different instructional models illustrate the flexibility of this approach. All content in the examples is taken from a course in COMAL-80 turtle graphics

Code Prediction by Feeding Trees to Transformers

We advance the state-of-the-art in the accuracy of code prediction (next token prediction) used in autocomplete systems. First, we report that using the recently proposed Transformer architecture even out-of-the-box outperforms previous neural and non-neural systems for code prediction. We then show that by making the Transformer architecture aware of the syntactic structure of code, we further increase the margin by which a Transformer-based system outperforms previous systems. With this, it outperforms the accuracy of an RNN-based system (similar to Hellendoorn et al. 2018) by 18.3\%, the Deep3 system (Raychev et al 2016) by 14.1\%, and an adaptation of Code2Seq (Alon et al., 2018) for code prediction by 14.4\%. We present in the paper several ways of communicating the code structure to the Transformer, which is fundamentally built for processing sequence data. We provide a comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Facebook internal Python corpus. Our code and data preparation pipeline will be available in open source

A new framework for the design and evaluation of a learning institution’s student engagement activities

In this article we explore the potential for attempts to encourage student engagement to be conceptualised as behaviour change activity, and specifically whether a new framework to guide such activity has potential value for the Higher Education (HE) sector. The Behaviour Change Wheel (BCW) (Michie, Susan, Maartje M van Stralen, and Robert West. 2011. “The Behaviour Change Wheel: A New Method for Characterising and Designing Behaviour Change Interventions.” Implementation Science : IS 6 (1): 42. doi:10.1186/1748-5908-6-42) is a framework for the systematic design and development of behaviour change interventions. It has yet to be applied to the domain of student engagement. This article explores its potential, by assessing whether the BCW comprehensively aligns with the state of student engagement as currently presented in the HE literature. This work achieves two things. It firstly allows a prima facie assessment of whether student engagement activity can be readily aligned with the BCW framework. It also highlights omissions and prevalence of activity types in the HE sector, compared with other sectors where behaviour change practice is being successfully applied