Abstract. In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of machine learning. Much of this work has focussed on the mammalian visual cortex, treating it as a hierarchically-structured pattern recognition machine that exploits statistical regularities in retinal input. It has further been proposed that the neocortex represents sensory information probabilistically, using some form of Bayesian inference to disambiguate noisy data. In the current paper, we focus on a particular model of the neocortex developed by Hawkins, known as hierarchical temporal memory (HTM). Our aim is to evaluate an important and recently implemented aspect of this model, namely its ability to represent temporal sequences of input within a hierarchically structured vector quantization algorithm. We test this temporal pooling feature of HTM on a benchmark of cursive handwriting recognition problems and compare it to a current state-of-the-art support vector machine implementation. We also examine whether two pre-processing techniques can enhance the temporal pooling algorithm's performance. Our results show that a relatively simple temporal pooling approach can produce recognition rates that approach the current state-of-the-art without the need for extensive tuning of parameters. We also show that temporal pooling performance is surprisingly unaffected by the use of preprocessing techniques.

Flipped classrooms are an instructional strategy that is becoming popular in educational contexts, particularly higher education. The principle of Flipped Classroom is that events that have traditionally taken place inside the classroom now take place outside the classroom and vice versa. Various studies have reported increased student performance and satisfaction after switching to a flipped classroom. However, most of these studies are based on students' perceptions of their own learning, not based on teachers' assessment of students' achievements. This article presents the results of flipping a computer programming course. It first describes how this course was flipped, then it presents the results of comparing the average marks awarded to students between those that took the course offering in flipped mode and those that took the course in the traditional mode. The comparison showed an increase in student performance in a flipped mode. Furthermore, the increase in student performance was sustained for 3 years, which is the full duration of this study. The comparison of student satisfaction showed an increase in student satisfaction in one campus, while the student satisfaction remained steady in another campus.

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