Syntactic generation of practice novice programs in Python

Abstract: In the present day, computer programs are written in high level languages and parsed syntactically as part of a compilation process. These parsers are defined with context-free grammars (CFGs), a language recogniser for the respective programming language. Formal grammars in general are used for language recognition or generation. In this paper, we present the automatic generation of procedural programs in Python using a CFG. We have defined CFG rules to model program templates and implemented these rules to produce infinitely many distinct practice programs in Python. Each generated program is designed to test a novice programmer’s knowledge of functions, expressions, loops, and/or conditional statements. The CFG rules are highly generic and can be extended to generate programs in other procedural languages. The resulting programs can be used as practice, test or examination problems in introductory programming courses. 500,000 iterations of generated programs can be found at: https://tinyurl.com/ pythonprogramgenerator. A survey of 103 students’ perception showed that 93.1% strongly agreed that these programs can help them in practice and improve their programming skills

Capturing AIS Behavior Using xAPI-like Statements

In this paper, we consider a minimalistic and behavioristic view of AIS to enable a standardizable mapping of both the behavior of the system and of the learner. In this model, the learners interact with the learning resources in a given learning environment following preset steps of learning processes. From this foundation, we make several subsequent arguments. (1) All intelligent digital resources such as intelligent tutoring systems (ITS) need to be well-documented with standardized metadata scheme. We propose a learning science extension of IEEE learning object metadata (LOM). specifically, we need to consider cognitive learning principles that have been used in creating the intelligent digital resources. (2) We need to consider AIS as whole when we record system behavior. Specifically, we need to record all four components delineated above (the learners, the resources, the environments, and the processes). We point to selected learning principles from the literature as examples for implementation of this approach. We concretize this approach using AutoTutor, a conversation-based ITS, serving as a typical intelligent digital resource

Cryptogenic Liver Disease in Four Children: A Novel Congenital Disorder of Glycosylation

n/

Rationale, design, implementation, and baseline characteristics of patients in the DIG trial: A large, simple, long-term trial to evaluate the effect of digitalis on mortality in heart failure

This article provides a detailed overview of the rationale for key aspects of the protocol of the Digitalis Investigation Group (DIG) trial. It also highlights unusual aspects of the study implementation and the baseline characteristics. The DIG trial is a large, simple, international placebo-controlled trial whose primary objective is to determine the effect of digoxin on all cause mortality in patients with clinical heart failure who are in sinus rhythm and whose ejection fraction is less than or equal to 0.45. An ancillary study examines the effect in those with an ejection fraction > 0.45. Key aspects of the trial include the simplicity of the design, broad eligibility criteria, essential data collection, and inclusion of various types of centers. A total of 302 centers in the United States and Canada enrolled 7788 patients between February 1991 and September 1993. Follow-up continued until December 1995 with the results available in Spring 1996