  Many students in introductory programming courses fare poorly in the code
writing tasks of the final summative assessment. Such tasks are designed to
assess whether novices have developed the analytical skills to translate from
the given problem domain to coding. In the past researchers have used
instruments such as code-explain and found that the extent of cognitive depth
reached in these tasks correlated well with code writing ability. However, the
need for manual marking and personalized interviews used for identifying
cognitive difficulties limited the study to a small group of stragglers. To
extend this work to larger groups, we have devised several question types with
varying cognitive demands collectively called Algorithmic Reasoning Tasks
(ARTs), which do not require manual marking. These tasks require levels of
reasoning which can define a learning trajectory. This paper describes these
instruments and the machine learning models used for validating them. We have
used the data collected in an introductory programming course in the
penultimate week of the semester which required attempting ART type instruments
and code writing. Our preliminary research suggests ART type instruments can be
combined with specific machine learning models to act as an effective learning
trajectory and early prediction of code-writing skills.
