Using Concept Inventories to Measure Understanding

Measuring understanding is notoriously difficult. Indeed, in formulating learning outcomes the word “understanding” is usually avoided, but in the sciences, developing understanding is one of the main aims of instruction. Scientific knowledge is factual, having been tested against empirical observation and experimentation, but knowledge of facts alone is not enough. There are also models and theories containing complex ideas and inter-relationships that must be understood, and considerable attention has been devoted across a range of scientific disciplines to measuring understanding. This case study will focus on one of the main tools employed: the concept inventory and in particular the Force Concept Inventory. The success of concept inventories in physics has spawned concept inventories in chemistry, biology, astronomy, materials science and maths, to name a few. We focus here on the FCI, ask how useful concept inventories are for evaluating learning gains. Finally, we report on recent work by the authors to extend conceptual testing beyond the multiple-choice format

The Role of Artificial Intelligence in Educating Novice Programmers

Programming is an inherently difficult skill to acquire and develop. Those who attempt to learn programming may be easily discouraged. The current landscape for computer science education does not address the needs of every novice programmer. Literature reports a discrepancy between student misconceptions and instructors’ perceptions of those misconceptions. Those who can afford a one-on-one human tutor perform on average two standard deviations better than those who learn via conventional methods, suggesting there is a need for a comparable, cheaper replacement. As a result, a number of intelligent tutoring systems have been developed for the purpose of teaching introductory programming concepts and replicating the benefits of one-on-one human tutoring. In this thesis, we analyze and discuss the literature pertaining to student misconceptions, selecting five fundamental misconception categories for introductory programming to demonstrate the effectiveness of existing intelligent tutoring systems. The features of existing intelligent tutoring systems are discussed and analyzed with respect to their effectiveness in addressing student misconceptions. Finally, we highlight the current gap in research on intelligent tutoring systems, hypothesizing the architecture and features of an ideal intelligent tutoring system for introductory programming.Electrical and Computer Engineerin

Early computing and data processing in Malta

Malta has been a heavy user of computers only since the 1980s, following the availability of the personal computer. Before that date, the diffusion of computers in Malta was slow. This paper describes the supply and application of computers from the late 1960s until the early 1980s. The state of computing and the slow take-up of computers is analysed and explained. The paper concludes with an explanation for 'fake off' in the 1980s.peer-reviewe

Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences