Promoting Programming Learning. Engagement, Automatic Assessment with Immediate Feedback in Visualizations

The skill of programming is a key asset for every computer science student. Many studies have shown that this is a hard skill to learn and the outcomes of programming courses have often been substandard. Thus, a range of methods and tools have been developed to assist students’ learning processes. One of the biggest fields in computer science education is the use of visualizations as a learning aid and many visualization based tools have been developed to aid the learning process during last few decades. Studies conducted in this thesis focus on two different visualizationbased tools TRAKLA2 and ViLLE. This thesis includes results from multiple empirical studies about what kind of effects the introduction and usage of these tools have on students’ opinions and performance, and what kind of implications there are from a teacher’s point of view. The results from studies in this thesis show that students preferred to do web-based exercises, and felt that those exercises contributed to their learning. The usage of the tool motivated students to work harder during their course, which was shown in overall course performance and drop-out statistics. We have also shown that visualization-based tools can be used to enhance the learning process, and one of the key factors is the higher and active level of engagement (see. Engagement Taxonomy by Naps et al., 2002). The automatic grading accompanied with immediate feedback helps students to overcome obstacles during the learning process, and to grasp the key element in the learning task. These kinds of tools can help us to cope with the fact that many programming courses are overcrowded with limited teaching resources. These tools allows us to tackle this problem by utilizing automatic assessment in exercises that are most suitable to be done in the web (like tracing and simulation) since its supports students’ independent learning regardless of time and place. In summary, we can use our course’s resources more efficiently to increase the quality of the learning experience of the students and the teaching experience of the teacher, and even increase performance of the students. There are also methodological results from this thesis which contribute to developing insight into the conduct of empirical evaluations of new tools or techniques. When we evaluate a new tool, especially one accompanied with visualization, we need to give a proper introduction to it and to the graphical notation used by tool. The standard procedure should also include capturing the screen with audio to confirm that the participants of the experiment are doing what they are supposed to do. By taken such measures in the study of the learning impact of visualization support for learning, we can avoid drawing false conclusion from our experiments. As computer science educators, we face two important challenges. Firstly, we need to start to deliver the message in our own institution and all over the world about the new – scientifically proven – innovations in teaching like TRAKLA2 and ViLLE. Secondly, we have the relevant experience of conducting teaching related experiment, and thus we can support our colleagues to learn essential know-how of the research based improvement of their teaching. This change can transform academic teaching into publications and by utilizing this approach we can significantly increase the adoption of the new tools and techniques, and overall increase the knowledge of best-practices. In future, we need to combine our forces and tackle these universal and common problems together by creating multi-national and multiinstitutional research projects. We need to create a community and a platform in which we can share these best practices and at the same time conduct multi-national research projects easily.Siirretty Doriast

A Data-Driven Approach to Compare the Syntactic Difficulty of Programming Languages

Educators who teach programming subjects are often wondering “which programming language should I teach first?”. The debate behind this question has a long history and coming up with a definite answer to this question would be farfetched. Nonetheless, several efforts can be identified in the literature wherein pros and cons of mainstream programming languages are examined, analysed, and discussed in view of their potential to facilitate the didactics of programming concepts especially to novice programmers. In line with these efforts, we explore the latter question by comparing the syntactic difficulty of two modern, but fundamentally different, programming languages: Java and Python. To achieve this objective, we introduce a standalone and purely data-driven method which stores the code submissions and clusters the errors occurred under the aid of a custom transition probability matrix. For the evaluation of this model a total of 219,454 submissions, made by 715 first-year undergraduate students, in 259 unique programming exercises were gathered and analysed. The results indicate that Python is an easier-to-grasp programming language and is, therefore, highly recommended as the steppingstone in introductory courses. Besides, the adoption of the described method enables educators to not only identify those students who struggle with coding (syntax-wise) but further paves the pathway for the adoption of personalised and adaptive learning practices

Long term effects on technology enhanced learning : The use of weekly digital lessons in mathematics

In this study we investigate the effects of long-term technology enhanced learning (TEL) in mathematics learning performance and fluency, and how technology enhanced learning can be integrated into regular curriculum. The study was conducted in five second grade classes. Two of the classes formed a treatment group and the remaining three formed a control group. The treatment group used TEL in one mathematics lesson per week for 18 to 24 months. Other lessons were not changed. The difference in learning performance between the groups tested using a post-test; for that, we used a mathematics performance test and a mathematics fluency test. The results showed that the treatment group using TEL got statistically significantly higher learning performance results compared to the control group. The difference in arithmetic fluency was not statistically significant even though there was a small difference in favor of the treatment group. However, the difference in errors made in the fluency test was statistically significant in favor of the treatment group.Peer reviewe

Cultural Issues That Affect Computer Programming: A Study of Vietnamese in Higher Education

Every society has its own cultural system which ultimately permeates and influences their educational system. Cultural attitudes impact the way students learn and participate in education. A few social practices or culture values may affect student engagement, learning process and learning experience in computer programming education. The purpose of this research was to investigate the impact of students&rsquo; cultural attitudes on learning programming courses in Vietnam. To identify this, a questionnaire was designed and distributed to Vietnamese undergraduate IT students in order to measure the relationship between cultural attitudes and student engagement in learning. This study used Geert Hofstede&rsquo;s defined cultural dimensions of power distance, and collectivism versus individualism as combined factors that affect student participation and engagement. The survey results were analyzed by the correlation coefficient statistical method to examine the statistical relationships that exist between student&rsquo;s power dependency culture and participation in learning. The survey results confirmed that many students completed their secondary education in a teacher centered environment and students are hesitant to express their opinions to teachers, and prefer to use existing solutions to complete their work. Moreover, statistical results partially supported the conclusion that, student&rsquo;s high power distance culture may affect their engagement in learning programming courses though many students are interested in active learning environments.</p

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine learning model developed using ongoing formative assessment scores as indicators of student engagement. Visualisation of the classification tree results is employed as student engagement indicators for instructors to observe and intervene with students. The results of this study showed that ongoing assessment is related to student engagement and subsequent final programming exam performance and possible to identify students at-risk of failing the final exam. Finally, our study identified students impacted by the Covid-19 pandemic. These were students who attended the final programming exam in the semester 2019-2020 and who scored well in formative assessments. Based on these results we present a simple student engagement indicator and its potential application as a student progress monitor for early identification of students at risk.</p

Long term effects on technology enhanced learning: The use of weekly digital lessons in mathematics

In this study we investigate the effects of long-term technology enhanced learning (TEL) in mathematics learning performance and fluency, and how technology enhanced learning can be integrated into regular curriculum. The study was conducted in five second grade classes. Two of the classes formed a treatment group and the remaining three formed a control group. The treatment group used TEL in one mathematics lesson per week for 18 to 24 months. Other lessons were not changed. The difference in learning performance between the groups tested using a post-test; for that, we used a mathematics performance test and a mathematics fluency test. The results showed that the treatment group using TEL got statistically significantly higher learning performance results compared to the control group. The difference in arithmetic fluency was not statistically significant even though there was a small difference in favor of the treatment group. However, the difference in errors made in the fluency test was statistically significant in favor of the treatment group.  </div

ARLEAN: An Augmented Reality Learning Analytics Ethical Framework

The emergence of the Learning Analytics (LA) field contextualised the connections in various disciplines and the educational sector, acted as a steppingstone toward the reformation of the educational scenery, thus promoting the importance of providing users with adaptive and personalised learning experiences. At the same time, the use of Augmented Reality (AR) applications in education have been gaining a growing interest across all the educational levels and contexts. However, the efforts to integrate LA techniques in immersive technologies, such as AR, are limited and scarce. This inadequacy is mainly attributed to the difficulties that govern the collection and interpretation of the primary data. To deal with this shortcoming, we present the “Augmented Reality Learning Analytics” (ARLEAN) ethical framework, tailored to the specific characteristics that AR applications have, and focused on various learning subjects. The core of this framework blends the technological, pedagogical, and psychological elements that influence the outcome of educational interventions, with the most widely adopted LA techniques. It provides concrete guidelines to educational technologists and instructional designers on how to integrate LA into their practices to inform their future decisions and thus, support their learners to achieve better results.</p

Limits and Virtues of Educational Technology in Elementary School Mathematics

The vast development and expansion of educational technology has led to the deconstruction of various barriers and challenges that the traditional approaches were incapable of overcoming. However, a field that still holds a great deal of interest and needs to be strengthened concerns the practices that surround mathematics education. Digital learning tools can provide a partial solution to this problem. In this study, we introduce a curriculum-driven digital practicing tool which accommodates diverse learning styles and educational needs. The representative sample of the pilot included 135 third-grade primary school students, randomly split in two groups, who participated in the 8-week experiment. The findings suggest that the digital practicing path can facilitate the development of subject mastery and increase the accuracy of arithmetic fact calculations. In addition, the use of learning analytics tools can facilitate the knowledge acquisition process and prevent learners from developing misconceptions toward the learning subject.</p

Prediction of Student Final Exam Performance in an Introductory Programming Course: Development and Validation of the Use of a Support Vector Machine-Regression Model

This paper presents a Support Vector Machine predictive model to determine if prior programming knowledge and completion of in-class and take home formative assessment tasks might be suitable predictors of examination performance. Student data from the academic years 2012 - 2016 for an introductory programming course was captured via ViLLE e-learning tool for analysis. The results revealed that student prior programming knowledge and assessment scores captured in a predictive model, is a good fit of the data. However, while overall success of the model is significant, predictions on identifying at-risk students is neither high nor low and that persuaded us to include two more research questions. However, our preliminary post analysis on these test results show that on average students who secured less than 70% in formative assessment scores with little or basic prior programming knowledge in programming may fail in the final programming exam and increase the prediction accuracy in identifying at-risk students from 46% to nearly 63%. Hence, these results provide immediate information for programming course instructors and students to enhance teaching and learning process.</p