Student Monks – Teaching Recursion in an IS or CS Programming Course Using the Towers of Hanoi

Educators have been using the Towers of Hanoi problem for many years as an example of a problem that has a very elegant recursive solution. However, the elegance and conciseness of this solution can make it difficult for students to understand the amount of computer time required in the execution of this solution. And, like many recursive computer programs, students often find it difficult to follow a trace of the solution. Research in computer education has shown that active learning exercises achieve positive educational results. In line with this research, an active learning exercise was employed in the classroom to assist students in gaining a better understanding of the recursive solution to the Towers of Hanoi problem. This demonstration can be used in an introductory IS or CS programming class, independent of the language used. The demonstration involves using student volunteers, who, in the demonstration, are referred to as “monks,” a reference to the original problem that had monks moving the golden rings in the Towers of Hanoi. An anonymous student survey revealed that students felt strongly that the demonstration helped them better understand recursion, and that the demonstration was a good use of class time. In addition, an analysis of a small sample of students’ computer programs following the demonstration, suggests that there may be pedagogical benefits to use of the student monk demonstration

Language Design for Reactive Systems: On Modal Models, Time, and Object Orientation in Lingua Franca and SCCharts

Reactive systems play a crucial role in the embedded domain. They continuously interact with their environment, handle concurrent operations, and are commonly expected to provide deterministic behavior to enable application in safety-critical systems. In this context, language design is a key aspect, since carefully tailored language constructs can aid in addressing the challenges faced in this domain, as illustrated by the various concurrency models that prevent the known pitfalls of regular threads. Today, many languages exist in this domain and often provide unique characteristics that make them specifically fit for certain use cases. This thesis evolves around two distinctive languages: the actor-oriented polyglot coordination language Lingua Franca and the synchronous statecharts dialect SCCharts. While they take different approaches in providing reactive modeling capabilities, they share clear similarities in their semantics and complement each other in design principles. This thesis analyzes and compares key design aspects in the context of these two languages. For three particularly relevant concepts, it provides and evaluates lean and seamless language extensions that are carefully aligned with the fundamental principles of the underlying language. Specifically, Lingua Franca is extended toward coordinating modal behavior, while SCCharts receives a timed automaton notation with an efficient execution model using dynamic ticks and an extension toward the object-oriented modeling paradigm

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus