2,175 research outputs found

    Introductory programming: a systematic literature review

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    As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

    Visual and Textual Programming Languages: A Systematic Review of the Literature

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    It is well documented, and has been the topic of much research, that Computer Science courses tend to have higher than average drop out rates at third level. This is a problem that needs to be addressed with urgency but also caution. The required number of Computer Science graduates is growing every year but the number of graduates is not meeting this demand and one way that this problem can be alleviated is to encourage students at an early age towards studying Computer Science courses. This paper presents a systematic literature review on the role of visual and textual programming languages when learning to program, particularly as a first programming language. The approach is systematic, in that a structured search of electronic resources has been conducted, and the results are presented and quantitatively analysed. This study will give insight into whether or not the current approaches to teaching young learners programming are viable, and examines what we can do to increase the interest and retention of these students as they progress through their education.Comment: 18 pages (including 2 bibliography pages), 3 figure

    Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

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    Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952, R01-DC007683); National Science Foundation (IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-01-1-0624

    The abstraction transition taxonomy: developing desired learning outcomes through the lens of situated cognition

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    We report on a post-hoc analysis of introductory programming lecture materials. The purpose of this analysis is to identify what knowledge and skills we are asking students to acquire, as situated in the activity, tools, and culture of what programmers do and how they think. The specific materials analyzed are the 133 Peer Instruction questions used in lecture to support cognitive apprenticeship -- honoring the situated nature of knowledge. We propose an Abstraction Transition Taxonomy for classifying the kinds of knowing and practices we engage students in as we seek to apprentice them into the programming world. We find students are asked to answer questions expressed using three levels of abstraction: English, CS Speak, and Code. Moreover, many questions involve asking students to transition between levels of abstraction within the context of a computational problem. Finally, by applying our taxonomy in classifying a range of introductory programming exams, we find that summative assessments (including our own) tend to emphasize a small range of the skills fostered in students during the formative/apprenticeship phase

    A pilot study on the impact of teaching assistant led CS1 study sessions using Peer Instruction

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    James Madison University’s Computer Science program strives to be a student-centered learning environment with a focus on creating a community for undergraduate success. National data reveals computer science has the lowest student retention rate compared to other STEM majors. The National Center for Women and Information Technology (NCWIT) has compiled a list of ways to retain students in Computer Science. In particular, NCWIT calls for collaboration indicate that “a sense of belonging, or a feeling of fit, is important for supporting student interest and persistence.” One aspect of creating community is the department’s longstanding commitment to provide undergraduate teaching assistants (TAs). Traditionally, TAs provide one-on-one help in the classroom and also hold supplementary lab hours in the evenings to answer questions. As part of this honors project, we have developed a new program called “The Fourth Hour” to increase student retention. Led by TAs using Peer Instruction (PI), these weekly study sessions provide a structured review of introductory topics. The aim of this research is to discover if weekly study sessions promote an environment in which students feel an increased sense of belonging and improved course material retention. In the Fall 2019 semester, JMU offered ten sections of CS149, the introductory programming course, also known as “CS1” in the literature. Each section had approximately 30 students enrolled. Four TAs were chosen to lead one study session each week using the same lesson materials. Three attitudinal surveys were administered over the duration of the semester to collect data on student demographics, self-efficacy, and sense of belonging. Pre- and post assessment results were recorded to test student course material retention. Study session attendance was also collected to discern if there was a correlation with student demographics, self-efficacy, sense of belonging, and/or course material retention. Anomalies in the data and inconsistent attendance rates limited the statistical significance of our results. However, our qualitative analysis suggests that the study sessions had a positive impact on students. As a result, the CS department is planning to continue offering the Fourth Hour program
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