Exploring Computer Literacy Variance: Insights from an Introductory Statistical Programming Class

This Innovative-Practice Full Paper presents the curriculum development of an introductory course in programming and data science for postdoctoral researchers (PDRs) in the biosciences. The use of computing software has become ubiquitous and a working knowledge of data science has become increasingly essential for researchers in all domains. However, curriculum development focusing on imparting foundational programming skills and fundamentals of data science for researchers in domains other than computing has been scarce. Thus, there is an unmet need for curriculum development involving computational thinking, programming, and the fundamentals of data science for this audience. Recognizing these growing needs and demands of researchers to learn programming and data science that can then be applied to their area of research or practice, we developed an introductory course in programming and data science for PDRs in biology and medicine. The primary goal of the course was to develop computational thinking skills in PDRs who hail from backgrounds that have traditionally not focused on inculcating computational thinking. This course covered the fundamental concepts of programming using either Python or R - languages that researchers outside the computing community use in numerous ways including the statistical analysis of large datasets that are becoming increasingly common in biomedical research. Further, PDRs enrolled in the course were introduced to some of the broad categories of problems in data science - exploratory data analysis, classification, regression, and clustering - along with relevant algorithms and how they can be applied to real-world datasets in their respective domains using packages or libraries in Python or R. We also report the feedback from the enrolled PDRs, lessons learned, and recommendations for instructors interested in designing similar curricula. Our course focusing on computing and data science education for postdoctoral scholars from a non-computing background demonstrates a promising model for incorporating computing education in other areas of study that do not traditionally have a focus on computing education as well as in continuing education.

Several instruments have been used for assessing Computational Thinking (CT) abilities. In this exploratory and preliminary study, we investigate how appropriate the Bebras challenge is as an instrument to assess and measure CT abilities. Bebras is an international challenge whose goal is to promote Computer Science and CT. The test can be answered without any prior knowledge on computer science. Our broad research question is whether we can evolve Bebras into a full fledged instrument to assess and measure CT abilities. In this paper, we instantiate a few more specific research questions: Is Bebras performance a good predictor of success for students within programming courses? Is there any correlation between Bebras performance and students' grades? Do students improve their performance in Bebras tests when exposed to the contents of a programming course? Our dataset consists of the grades of 138 students who attended introductory programming courses at two Brazilian universities and their performance in two simulated Bebras tests. The first test was applied at the beginning of the term and therefore before any exposition to programming classes. The second one was applied at the end of the term. The results suggest that the performance on Bebras is only moderately correlated to the student grades. We conclude that it is not very likely that CT measures can be derived from the Bebras test as it is currently designed. While further research is needed on how we can leverage the Bebras effort to extend it into a CT assessment instrument, we performed a preliminary study on the use of Item Response Theory (IRT) as a means to improve the selection of questions and the design of the test. We expect the results of this research can contribute both to the development and discussion on CT assessment as well as to the Bebras effort to educate CT.

Today’s digital society has turned the development of students’ computational thinking capabilities into a critical factor for their future success. As higher education institutions, we need to take responsibility for this development in every degree course we offer, and provide students with the kind of subjects and activities that best contribute to this aim. In this paper, we study the impact of following an introductory programming course on the development of the computational thinking capabilities of university students. In order to achieve this aim, a concurrent cohort observational study was carried out in which we measured both the subjective and objective computational thinking capabilities of 104 participants (50 first year students enrolled on a Bachelor’s degree course in Psychology at the Catholic University of Murcia (UCAM), and 54 first year students enrolled on a Bachelor’s degree course in Health Information Systems at the University of Alicante (UA)). The statistical procedures applied to test our hypotheses were a two-way mixed ANOVA, a paired-sample T-test and an independent-sample T-test. The data shows that the group at UA had an initial higher subjective perception of their computational capabilities than the group at UCAM. This perception was supported by their objective scores, which were also significantly higher. However, the subjective assessment of computational capability of the UA group diminished after exposure to the programming course, contrasting with the fact that their objective computational capabilities improved significantly. In the UCAM group, both subjective and objective capabilities remained constant over time. Based on these results, we can conclude that computational thinking capabilities are not developed naturally, but need to be trained. Providing such training to all our students, and not only to those enrolled on undergraduate degrees in engineering, is of paramount importance to allow them to face the challenges of their future professions. This paper empirically demonstrates the extent to which exposing subjects to a programming course may contribute to this aim.

The purpose of this study was to deduct guidelines from an introductory programming course to understand the critical points based on the opinions of the students. These critical points could be a guide for future course designs. An introductory visual programming course was designed for novice learners during 2014, fall term at Middle East Technical University, Turkey. Qualitative data were collected with interviews and observations. From the interviews, five themes emerged: communication, computational thinking, environment, motivation, and course recommendations. Results of the study revealed what motivates students, what parts of the course students found useful, and what parts should be replaced. An environment which is easy, visual, and communicative through an informal interface could be useful, especially in terms of motivation. Additionally, examples with useful products rather than meaningless algorithm examples could motivate students better. Interviews also revealed topics students found to be difficult. Results of this study could be a guide for future visual programming course designs.

How can unplugged approach facilitate novice students’ understanding of computational thinking? An exploratory study from a Nigerian university

This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students' computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners' groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.

The study aims to research the effect of an introductory programming course equipped with math-based programming activities on computational thinking skills and self-efficacy. A static-groups pre-test post-test quasi-experimental design was used. One hundred seventy-six 6th-grade students of a public school participated in the study. Eighty-nine of these students were in the experimental group, and 87 were in the control group. While the students in the experimental group received introductory programming education with Math-supported activities, the students in the control group received programming education with traditional course activities. Equivalent programming activities were carried out in both groups. Data were collected via two scales; Computational Thinking Test and Self-Efficacy Perception Scale for Computational Thinking Skills. After the study was completed, post-test scores were analyzed by ANCOVA analysis by controlling pre-test scores. The findings indicated no difference between the two groups regarding computational thinking test performance. Similarly, no conclusion stated a difference between the groups’ perceptions of self-efficacy of computational thinking. According to these results, evaluations regarding the positive and negative effects of using mathematics and programming together in an elementary programming education, which is thought to be related to Computational Thinking Skills, were reached at the skills of the study.

This Research to Practice Full Paper presents our experience in teaching an introductory programming course in Python by using a visual programming development environment based on flow-charts and active learning with an interactive eBook. The field of computer science education is always being challenged with the high attrition rates despite the ever growing industry demand for computing expertise. The lower rate of student retention is often associated with considerable dropout and failure rates in introductory programming courses during the freshmen year. The main challenge is getting students to write meaningful programs in a short time by focusing more on computational thinking principles and less on language details at that point in time. Nowadays, most students prefer to be engaged and discover course content through exploration, interaction, and collaboration that is relevant, useful, and fun compared to traditional blackboard-based lecturing styles. For evaluation of learning outcomes based on the quantifiable criteria with robust statistical analysis, eleven sections of the course over three semesters were considered. The initial evaluation of summative assessment and analysis of a survey result enable us to conclude that the proposed instructional approach increases student engagement, facilitate learning and contribute to the progress of students in this course.

BackgroundWith the increasing interdisciplinarity between computer science (CS) and other fields, a growing number of non-CS students are embracing programming. However, there is a gap in research concerning differences in programming learning between CS and non-CS students. Previous studies predominantly relied on outcome-based assessments, focusing on summative evaluations and surveys while providing little insight into the real learning process and differences therein. This study aims to provide a process-oriented comparison of programming learning between two novice student groups, CS and Math, under uniform instructional conditions, focusing on their semester-long scores, engagement, and code metrics.ResultsOur research involves 75 novice students enrolled in a compulsory introductory programming course designed for a mixed class, comprising 35 Math and 40 CS. Through Latent Class Analysis and Self-Organizing Maps, we identify distinct learning states throughout the semester and employ sequence mining to explore the differences in learning trajectories and state transitions between the two cohorts. Our results reveal that the association between engagement and scores diverges across different majors as the course progresses, deviating from the widely discussed positive correlation. In the semester-long code metrics analysis of students exhibiting over-engineering state, the two cohorts display opposing trends. Moreover, CS students demonstrate significant alignment between formative and summative scores, whereas Math peers exhibit phenomena of cold-start and learning avoidance.ConclusionsThis study underscores the importance of understanding distinct learning trajectories to improve instructional design for diverse learner groups. Our findings indicate that CS students follow increasingly efficient learning patterns with decreasing code complexity over time, while Math students need strategies to overcome phenomena of cold-start and learning avoidance. Code metrics can provide valuable insights into students’ programming performance and patterns. The research also highlights the importance of active engagement and fostering computational thinking in the early stages. Based on these insights, we propose recommendations for instructional design to better support students in introductory programming courses. This study also makes a methodological contribution to the process-oriented research in programming education.

We experimented with the viability of using Lego Mindstorms as an alternative approach to supplement the learning of Basic Programming among novice university students. The study captured reactions and responses from ten undergraduates who had no background in Programming and were facing difficulties coping with an introductory course in Programming. Four Lego Mindstorms units were deployed to examine the participants' problem-solving and computational thinking skills. Findings indicate a high positive response (t=3.88 and p =0.004 for Problem Solving; t=-4.13 and p=0.003 for Computational thinking) to the use of Mindstorms as a strategy to encourage higher-order thinking, a critical skill in learning Programming. Participants reported clarity in understanding computational thinking concepts and claimed to be better able to articulate problem-solving tasks when prompted using Mindstorms.

Visual programming languages (VPLs), such as Scratch and StarLogo TNG, can make computer science education more accessible to everyone. Current researches in the study of using VPLs for educational purposes primarily focus on understanding motivational benefits and computational thinking gains. All these educational VPLs claim to scaffold students learning computational thinking concepts. Although the evaluations show that students may exhibit more enthusiasm, it is not clear what computational thinking concepts are actually learned by students. In this paper, we attempt to develop a visual programs recognition tool for student-created StarLogo TNG simulations which representing the computational thinking concepts implemented by the students. Through collecting student's created projects over time, this visual programs recognition tool can possibly indicate the patterns of computational thinking in science simulations created by StarLogo TNG.

A teaching approach plays an important role in teaching and learning process of an introductory programming (IP) course. The teaching approach should focus on different programming skills required by novice programmers. In this study, we introduced the teaching and learning approach based on an ADRI (Approach, Deployment, Result, Improvement) approach in the IP course which focuses on both programming knowledge (syntax and semantics) and problem solving strategies. We compared the teaching and learning approach of the IP course with the five levels of SOLO taxonomy. We assessed the students’ performance by using different assessment tasks based on the four stages of the ADRI approach. Results show that the current teaching and learning approach of the IP course addressed all the five levels of SOLO taxonomy. The students’ performance in the Approach and Result stages (82%) are good, and the performance in the Improvement (71%) and Deployment (69%) stages are satisfactory. Overall, the ADRI approach provides positive impact on the teaching and learning process of the IP course.

A teaching approach plays an important role in teaching and learning process of an introductory programming (IP) course. The teaching approach should focus on different programming skills required by novice programmers. In this study, we introduced the teaching and learning approach based on an ADRI (Approach, Deployment, Result, Improvement) approach in the IP course which focuses on both programming knowledge (syntax and semantics) and problem solving strategies. We compared the teaching and learning approach of the IP course with the five levels of SOLO taxonomy. We assessed the students’ performance by using different assessment tasks based on the four stages of the ADRI approach. Results show that the current teaching and learning approach of the IP course addressed all the five levels of SOLO taxonomy. The students’ performance in the Approach and Result stages (82%) are good, and the performance in the Improvement (71%) and Deployment (69%) stages are satisfactory. Overall, the ADRI approach provides positive impact on the teaching and learning process of the IP course.

A teaching approach plays an important role in teaching and learning process of an introductory programming (IP) course. The teaching approach should focus on different programming skills required by novice programmers. In this study, we introduced the teaching and learning approach based on an ADRI (Approach, Deployment, Result, Improvement) approach in the IP course which focuses on both programming knowledge (syntax and semantics) and problem solving strategies. We compared the teaching and learning approach of the IP course with the five levels of SOLO taxonomy. We assessed the students’ performance by using different assessment tasks based on the four stages of the ADRI approach. Results show that the current teaching and learning approach of the IP course addressed all the five levels of SOLO taxonomy. The students’ performance in the Approach and Result stages (82%) are good, and the performance in the Improvement (71%) and Deployment (69%) stages are satisfactory. Overall, the ADRI approach provides positive impact on the teaching and learning process of the IP course.

Aim/Purpose: This study introduced a new teaching and learning approach based on an ADRI (Approach, Deployment, Result, Improvement) model in an introductory programming (IP) course. The effectiveness of the new teaching and learning process was determined by collecting feedback from the IP instructors and by analyzing the final exam grades of the course. Background: Learning to program is considered a difficult and challenging task for a considerable number of novice programmers. As a result, high failure and dropout rates are often reported in IP courses. Different studies have been conducted to investigate the issue. One of the reasons for this challenge is the multiple skills that students have to master in order to be able to build programs. These skills include programming knowledge and problem-solving strategies and being able to pay equal attention to these required skills in the IP course. Methodology: A focus group was conducted to obtain feedback from the IP instructors about the ADRI approach. The performance of the students who had completed the IP course before ADRI was compared with those who used the ADRI approach by undertaking a comparative analysis of their final exam grades. Contribution: The study demonstrates that the new teaching and learning approach based on the ADRI model encourages students to pay equal attention to programming knowledge and problem-solving strategies, discouraging programming shortcuts and reducing high attrition rates (failure and dropout) in the IP course. Findings: The results of the focus group show that the instructors preferred the ADRI approach compared to the traditional approach. The final exam grades show that the students performed better in semesters which offered the ADRI approach as compared to those semesters without this approach. Future Research: Future research will explore the ADRI approach in other fields of computer science studies, such as database and data structure, to determine if its impact has a wider application than just teaching introductory programming.