104,594 research outputs found
Introductory programming: a systematic literature review
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
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
Improving the Quality of Technology-Enhanced Learning for Computer Programming Courses
Teaching computing courses is a major challenge for the majority of lecturers in Libyan higher learning institutions. These courses contain numerous abstract concepts that cannot be easily explained using traditional educational methods. This paper describes the rationale, design, development and implementation stages of an e-learning package (including multimedia resources such as simulations, animations, and videos) using the ASSURE model. This training package can be used by students before they attend practical computer lab sessions, preparing them by developing technical skills and applying concepts and theories presented in lecture through supplementary study and exercises
Implicit Theories and Self-efficacy in an Introductory Programming Course
Contribution: This study examined student effort and performance in an
introductory programming course with respect to student-held implicit theories
and self-efficacy. Background: Implicit theories and self-efficacy shed a light
into understanding academic success, which must be considered when developing
effective learning strategies for programming. Research Questions: Are implicit
theories of intelligence and programming, and programming-efficacy related to
each other and student success in programming? Is it possible to predict
student course performance using a subset of these constructs? Methodology: Two
consecutive surveys (N=100 and N=81) were administered to non-CS engineering
students in I\c{s}{\i}k University. Findings: Implicit theories and
self-beliefs are interrelated and correlated with effort, performance, and
previous failures in the course and students explain failure in programming
course with "programming-aptitude is fixed" theory, and also that programming
is a difficult task for themselves.Comment: Programming Education. 8 page
Links between the personalities, styles and performance in computer programming
There are repetitive patterns in strategies of manipulating source code. For
example, modifying source code before acquiring knowledge of how a code works
is a depth-first style and reading and understanding before modifying source
code is a breadth-first style. To the extent we know there is no study on the
influence of personality on them. The objective of this study is to understand
the influence of personality on programming styles. We did a correlational
study with 65 programmers at the University of Stuttgart. Academic achievement,
programming experience, attitude towards programming and five personality
factors were measured via self-assessed survey. The programming styles were
asked in the survey or mined from the software repositories. Performance in
programming was composed of bug-proneness of programmers which was mined from
software repositories, the grades they got in a software project course and
their estimate of their own programming ability. We did statistical analysis
and found that Openness to Experience has a positive association with
breadth-first style and Conscientiousness has a positive association with
depth-first style. We also found that in addition to having more programming
experience and better academic achievement, the styles of working depth-first
and saving coarse-grained revisions improve performance in programming.Comment: 27 pages, 6 figure
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Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)
Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track.
The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include:
• Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running.
• Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes.
• Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees).
• No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed.
Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted.
Papers accepted in 2016 fell into two categories.
• Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative.
• Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying.
Both types of paper are included in these proceedings
Effects of Automated Interventions in Programming Assignments: Evidence from a Field Experiment
A typical problem in MOOCs is the missing opportunity for course conductors
to individually support students in overcoming their problems and
misconceptions. This paper presents the results of automatically intervening on
struggling students during programming exercises and offering peer feedback and
tailored bonus exercises. To improve learning success, we do not want to
abolish instructionally desired trial and error but reduce extensive struggle
and demotivation. Therefore, we developed adaptive automatic just-in-time
interventions to encourage students to ask for help if they require
considerably more than average working time to solve an exercise. Additionally,
we offered students bonus exercises tailored for their individual weaknesses.
The approach was evaluated within a live course with over 5,000 active students
via a survey and metrics gathered alongside. Results show that we can increase
the call outs for help by up to 66% and lower the dwelling time until issuing
action. Learnings from the experiments can further be used to pinpoint course
material to be improved and tailor content to be audience specific.Comment: 10 page
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