First Year Computer Science Projects at Coventry University:Activity-led integrative team projects with continuous assessment.

We describe the group projects undertaken by first year undergraduate Computer Science students at Coventry University. These are integrative course projects: designed to bring together the topics from the various modules students take, to apply them as a coherent whole. They follow an activity-led approach, with students given a loose brief and a lot of freedom in how to develop their project. We outline the new regulations at Coventry University which eases the use of such integrative projects. We then describe our continuous assessment approach: where students earn a weekly mark by demonstrating progress to a teacher as an open presentation to the class. It involves a degree of self and peer assessment and allows for an assessment of group work that is both fair, and seen to be fair. It builds attendance, self-study / continuous engagement habits, public speaking / presentation skills, and rewards group members for making meaningful individual contributions.Comment: 4 pages. Accepted for presentation at CEP2

Exploring machine learning methods to automatically identify students in need of assistance

Copyright 2015 ACM. Methods for automatically identifying students in need of assistance have been studied for decades. Initially, the work was based on somewhat static factors such as students' educational background and results from various questionnaires, while more recently, constantly accumulating data such as progress with course assignments and behavior in lectures has gained attention. We contribute to this work with results on early detection of students in need of assistance, and provide a starting point for using machine learning techniques on naturally accumulating programming process data. When combining source code snapshot data that is recorded from students' programming process with machine learning methods, we are able to detect high- and low-performing students with high accuracy already after the very first week of an introductory programming course. Comparison of our results to the prominent methods for predicting students' performance using source code snapshot data is also provided. This early information on students' performance is beneficial from multiple viewpoints. Instructors can target their guidance to struggling students early on, and provide more challenging assignments for high-performing students. Moreover, students that perform poorly in the introductory programming course, but who nevertheless pass, can be monitored more closely in their future studies

Students' syntactic mistakes in writing seven different types of SQL queries and its application to predicting students' success

© 2016 ACM. The computing education community has studied extensively the errors of novice programmers. In contrast, little attention has been given to student's mistake in writing SQL statements. This paper represents the first large scale quantitative analysis of the student's syntactic mistakes in writing different types of SQL queries. Over 160 thousand snapshots of SQL queries were collected from over 2000 students across eight years. We describe the most common types of syntactic errors that students make. We also describe our development of an automatic classifier with an overall accuracy of 0.78 for predicting student performance in writing SQL queries

What Do We Think We Think We Are Doing?: Metacognition and Self-Regulation in Programming

Metacognition and self-regulation are popular areas of interest in programming education, and they have been extensively researched outside of computing. While computing education researchers should draw upon this prior work, programming education is unique enough that we should explore the extent to which prior work applies to our context. The goal of this systematic review is to support research on metacognition and self-regulation in programming education by synthesizing relevant theories, measurements, and prior work on these topics. By reviewing papers that mention metacognition or self-regulation in the context of programming, we aim to provide a benchmark of our current progress towards understanding these topics and recommendations for future research. In our results, we discuss eight common theories that are widely used outside of computing education research, half of which are commonly used in computing education research. We also highlight 11 theories on related constructs (e.g., self-efficacy) that have been used successfully to understand programming education. Towards measuring metacognition and self-regulation in learners, we discuss seven instruments and protocols that have been used and highlight their strengths and weaknesses. To benchmark the current state of research, we examined papers that primarily studied metacognition and self-regulation in programming education and synthesize the reported interventions used and results from that research. While the primary intended contribution of this paper is to support research, readers will also learn about developing and supporting metacognition and self-regulation of students in programming courses

Toward Predicting Success and Failure in CS2: A Mixed-Method Analysis

Factors driving success and failure in CS1 are the subject of much study but less so for CS2. This paper investigates the transition from CS1 to CS2 in search of leading indicators of success in CS2. Both CS1 and CS2 at the University of North Carolina Wilmington (UNCW) are taught in Python with annual enrollments of 300 and 150 respectively. In this paper, we report on the following research questions: 1) Are CS1 grades indicators of CS2 grades? 2) Does a quantitative relationship exist between CS2 course grade and a modified version of the SCS1 concept inventory? 3) What are the most challenging aspects of CS2, and how well does CS1 prepare students for CS2 from the student's perspective? We provide a quantitative analysis of 2300 CS1 and CS2 course grades from 2013--2019. In Spring 2019, we administered a modified version of the SCS1 concept inventory to 44 students in the first week of CS2. Further, 69 students completed an exit questionnaire at the conclusion of CS2 to gain qualitative student feedback on their challenges in CS2 and on how well CS1 prepared them for CS2. We find that 56% of students' grades were lower in CS2 than CS1, 18% improved their grades, and 26% earned the same grade. Of the changes, 62% were within one grade point. We find a statistically significant correlation between the modified SCS1 score and CS2 grade points. Students identify linked lists and class/object concepts among the most challenging. Student feedback on CS2 challenges and the adequacy of their CS1 preparations identify possible avenues for improving the CS1-CS2 transition.Comment: The definitive Version of Record was published in 2020 ACM Southeast Conference (ACMSE 2020), April 2-4, 2020, Tampa, FL, USA. 8 page

Solar Intensity X-Ray and Particle Spectrometer SIXS : Instrument Design and First Results

The Solar Intensity X-ray and particle Spectrometer (SIXS) on the BepiColombo Mercury Planetary Orbiter ("Bepi") measures the direct solar X-rays, energetic protons, and electrons that bombard, and interact with, the Hermean surface. The interactions result in X-ray fluorescence and scattering, and particle induced X-ray emission (PIXE), i.e. "glow" of the surface in X-rays. Simultaneous monitoring of the incident and emitted radiation enables derivation of the abundances of some chemical elements and scattering properties of the outermost surface layer of the planet, and it may reveal other sources of X-ray emission, due to, for example, weak aurora-like phenomena in Mercury's exosphere. Mapping of the Hermean X-ray emission is the main task of the MIXS instrument onboard BepiColombo. SIXS data will also be used for investigations of the solar X-ray corona and solar energetic particles (SEP), both in the cruise phase and the passes of the Earth, Venus and Mercury before the arrival at Mercury's orbit, and the final science phase at Mercury's orbit. These observations provide the first-ever opportunity for in-situ measurements of the propagation of SEPs, their interactions with the interplanetary magnetic field, and space weather phenomena in multiple locations throughout the inner solar system far away from the Earth, and more extensively at Mercury's orbit. In this paper we describe the scientific objectives, design and calibrations, operational principles, and scientific performance of the final SIXS instrument launched to the mission to planet Mercury onboard BepiColombo. We also provide the first analysis results of science observations with SIXS, that were made during the Near-Earth Commissioning Phase and early cruise phase operations in 2018-19, including the background X-ray sky observations and "first light" observations of the Sun with the SIXS X-ray detection system (SIXS-X), and in-situ energetic electron and proton observations with the SIXS Particle detection system (SIXS-P).Peer reviewe

Solar Intensity X-Ray and Particle Spectrometer SIXS: Instrument Design and First Results

The Solar Intensity X-ray and particle Spectrometer (SIXS) on the BepiColombo Mercury Planetary Orbiter ("Bepi") measures the direct solar X-rays, energetic protons, and electrons that bombard, and interact with, the Hermean surface. The interactions result in X-ray fluorescence and scattering, and particle induced X-ray emission (PIXE), i.e. "glow" of the surface in X-rays. Simultaneous monitoring of the incident and emitted radiation enables derivation of the abundances of some chemical elements and scattering properties of the outermost surface layer of the planet, and it may reveal other sources of X-ray emission, due to, for example, weak aurora-like phenomena in Mercury's exosphere. Mapping of the Hermean X-ray emission is the main task of the MIXS instrument onboard BepiColombo. SIXS data will also be used for investigations of the solar X-ray corona and solar energetic particles (SEP), both in the cruise phase and the passes of the Earth, Venus and Mercury before the arrival at Mercury's orbit, and the final science phase at Mercury's orbit. These observations provide the first-ever opportunity for in-situ measurements of the propagation of SEPs, their interactions with the interplanetary magnetic field, and space weather phenomena in multiple locations throughout the inner solar system far away from the Earth, and more extensively at Mercury's orbit. In this paper we describe the scientific objectives, design and calibrations, operational principles, and scientific performance of the final SIXS instrument launched to the mission to planet Mercury onboard BepiColombo. We also provide the first analysis results of science observations with SIXS, that were made during the Near-Earth Commissioning Phase and early cruise phase operations in 2018-19, including the background X-ray sky observations and "first light" observations of the Sun with the SIXS X-ray detection system (SIXS-X), and in-situ energetic electron and proton observations with the SIXS Particle detection system (SIXS-P)

Predicting Academic Performance: A Systematic Literature Review

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.Peer reviewe