Learning recursion: Multiple nested and crossed dependencies

Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from sequence input. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modelling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e., nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e., two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed

What’s Motivation Got to Do with It? A Survey of Recursion in the Computing Education Literature

One of the most challenging topics for both computing educators and students is recursion. Pedagogical approaches for teaching recursion have appeared in the computing education literature for over 30 years, and the topic has generated a significant body of work. Given its persistence, relatively little attention has been paid to student motivation. This article summarizes results on teaching and learning recursion explored by the computing education community, noting the relative lack of interest in motivation. It concludes by briefly discussing an approach to teaching recursion is appealing for students interested in web development

A Cooperative Development System for an Interactive Introductory Programming Course

We present a system for a cooperative development of computer programs that was created for the lab sessions of an introductory programming course at the University of Ljubljana, Slovenia. The system relieved the students from the tedious task of retyping programs developed by the teaching assistant and enabled them to cooperate with the teaching assistant in solving programming problems. We thus made the lab sessions more efficient and interactive and brought them closer to the spirit of active learning approaches

Confluent Orthogonal Drawings of Syntax Diagrams

We provide a pipeline for generating syntax diagrams (also called railroad diagrams) from context free grammars. Syntax diagrams are a graphical representation of a context free language, which we formalize abstractly as a set of mutually recursive nondeterministic finite automata and draw by combining elements from the confluent drawing, layered drawing, and smooth orthogonal drawing styles. Within our pipeline we introduce several heuristics that modify the grammar but preserve the language, improving the aesthetics of the final drawing.Comment: GD 201

The impact of adjacent-dependencies and staged-input on the learnability of center-embedded hierarchical structures

A theoretical debate in artificial grammar learning (AGL) regards the learnability of hierarchical structures. Recent studies using an AnBn grammar draw conflicting conclusions (Bahlmann and Friederici, 2006, De Vries et al., 2008). We argue that 2 conditions crucially affect learning AnBn structures: sufficient exposure to zero-level-of-embedding (0-LoE) exemplars and a staged-input. In 2 AGL experiments, learning was observed only when the training set was staged and contained 0-LoE exemplars. Our results might help understanding how natural complex structures are learned from exemplars

An investigation of a manipulative simulation in the learning of recursive programming

Recursion is a fundamentally important topic in Computer Sciences; Even so, it is often omitted in introductory courses, or discussed only briefly. This is likely due, at least in part, to the fact that teaching recursion has been difficult. Perhaps the biggest problem in teaching recursion is that there are few, if any, naturally existing examples of recursion in our lives. However, successful simulations have shown that the computer may hold the key to solving this problem. A simulation of recursion presented to students before formal classroom instruction can provide a foundation of concrete experiences to build upon. The challenge is to develop an appropriate simulation and lesson plan for introducing recursion to students early in their programming experience;This research reviews previous attempts at teaching recursion, including detailed lesson plans, mental models of recursion, and other simulations. Then, a new simulation and lesson plan for its use are described. The effectiveness of the simulation is studied using two groups of students enrolled in a college-level, introductory programming course. Results indicate that students who used the simulation as their first exposure to recursion gained a deeper understanding of recursion than students receiving a lecture-based introduction to recursion. Specifically, students who used the simulation required fewer attempts to complete a set of recursive programming exercises and performed better on a follow-up exam given six weeks after the experiment;This research concludes with a discussion of two important questions: How should students think about recursion and how do they think about recursion. The simulation\u27s strengths and shortcomings in fostering effective ways of thinking about recursion are also discussed

Processing multiple non-adjacent dependencies: evidence from sequence learning

Processing non-adjacent dependencies is considered to be one of the hallmarks of human language. Assuming that sequence-learning tasks provide a useful way to tap natural-language-processing mechanisms, we cross-modally combined serial reaction time and artificial-grammar learning paradigms to investigate the processing of multiple nested (A(1)A(2)A(3)B(3)B(2)B(1)) and crossed dependencies (A(1)A(2)A(3)B(1)B(2)B(3)), containing either three or two dependencies. Both reaction times and prediction errors highlighted problems with processing the middle dependency in nested structures (A(1)A(2)A(3)B(3-)B(1)), reminiscent of the 'missing-verb effect' observed in English and French, but not with crossed structures (A(1)A(2)A(3)B(1-)B(3)). Prior linguistic experience did not play a major role: native speakers of German and Dutch-which permit nested and crossed dependencies, respectively-showed a similar pattern of results for sequences with three dependencies. As for sequences with two dependencies, reaction times and prediction errors were similar for both nested and crossed dependencies. The results suggest that constraints on the processing of multiple non-adjacent dependencies are determined by the specific ordering of the non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved (i. e. two or three). Furthermore, these constraints may not be specific to language but instead derive from limitations on structured sequence learning.Netherlands Organisation of Scientific Research (NWO) [446-08-014]; Max Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviour; Fundacao para a Ciencia e Tecnologia (IBB/CBME, LA, FEDER/POCI) [PTDC/PSI-PCO/110734/2009]; Stockholm Brain Institute; Vetenskapsradet; Swedish Dyslexia Foundation; Hedlunds Stiftelse; Stockholm County Council (ALF, FoUU)info:eu-repo/semantics/publishedVersio