Learning From Errors: Exploring the Effectiveness of Enhanced Error Messages in Learning to Program

Error messages provided by the programming environments are often cryptic and confusing to learners. This study explored the effectiveness of enhanced programming error messages (EPEMs) in a Python-based introductory programming course. Participants were two groups of middle school students. The control group only received raw programming error messages (RPEMs) and had 35 students. The treatment group received EPEMs and had 33 students. During the class, students used an automated assessment tool called Mulberry to practice their programming skill. Mulberry automatically collected all the solutions students submitted when solving programming problems. Data analysis was based on 6339 student solutions collected by Mulberry. Our results showed that EPEMs did not help to reduce student errors or improve students’ performance in debugging. The ineffectiveness of EPEMs may result from reasons such as the inaccuracy of the interpreter’s error messages or students not reading the EPEMs. However, the viewpoint of productive failure may provide a better explanation of the ineffectiveness of EPEMs. The failures in coding and difficulties in debugging can be resources for learning. We recommend that researchers reconsider the role of errors in code and investigate whether and how failures and debugging contribute to the learning of programming.

Food webs and simple graphics.

Food webs are fundamental in much of ecology and there has been a steady increase in studying their structure and properties over the past 50 years, nowadays often utilizing molecular methods too. First, this chapter will create code to draw a food web, then it will introduce the package cheddar. The reason for learning how to produce your own is not just to improve programming skill and logical thinking, it also means you are in a position to customize your diagrams in ways that perhaps are not available in pre-written packages. A parasitoid foodweb example is given. In this example from Thailand, 22 braconid parasitoid wasps, representing a total of 9 species were associated with 22 lepidopteran hosts representing a total of 11 species using DNA barcoding.