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

Efficient Use of Teaching Technologies with Programming Education

Learning and teaching programming are challenging tasks that can be facilitated by using different teaching technologies. Visualization systems are software systems that can be used to help students in forming proper mental models of executed program code. They provide different visual and textual cues that help student in abstracting the meaning of a program code or an algorithm. Students also need to constantly practice the skill of programming by implementing programming assignments. These can be automatically assessed by other computer programs but parts of the evaluation need to be assessed manually by teachers or teaching assistants.There are a lot of existing tools that provide partial solutions to the practical problems of programming courses: visualizing program code, assessing student programming submissions automatically or rubrics that help keeping manual assessment consistent. Taking these tools into use is not straightforward. To succeed, the teacher needs to find the suitable tools and properly integrate them into the course infrastructure supporting the whole learning process. As many programming courses are mass courses, it is a constant struggle between providing sufficient personal guidance and feedback while retaining a reasonable workload for the teacher.This work answers to the question "How can the teaching of programming be effectively assisted using teaching technologies?" As a solution, different learning taxonomies are presented from Computer Science perspective and applied to visualization examples so the examples could be used to better support deeper knowledge and the whole learning process within a programming course. Then, different parts of the assessment process of programming assignments are studied to find the best practices in supporting the process, especially when multiple graders are being used, to maintain objectivity, consistency and reasonable workload in the grading.The results of the work show that teaching technologies can be a valuable aid for the teacher to support the learning process of the students and to help in the practical organization of the course without hindering the learning results or personalized feedback the students receive from their assignments. This thesis presents new visualization categories that allow deeper cognitive development and examples on how to integrate them efficiently into the course infrastructure. This thesis also presents a survey of computer-assisted assessment tools and assessable features for teachers to use in their programming assignments. Finally, the concept of rubric-based assessment tools is introduced to facilitate the manual assessment part of programming assignments

Retention in Introductory Programming

The introductory programming course is one of the very first courses that computer science students encounter. The course is challenging not only because of the content, but also due to the challenges related to finding a place in a new community. Many have little knowledge of what to expect from university studies, some struggle to adjust their study behavior to match the expected pace, and a few simply cannot attend instruction due to e.g. family or work constraints. As a consequence, a considerable number of students end up failing the course, or pass the course with substandard knowledge. This leads to students failing to proceed in their studies at a desirable pace, to students who struggle with the subsequent courses, and to students who completely drop out from their studies. This thesis explores the issue of retention in introductory programming courses through multiple viewpoints. We first analyze how the teaching approaches reported in literature affect introductory programming course pass rates. Then, changes on the retention at the University of Helsinki are studied using two separate approaches. The first approach is the use of a contemporary variant of Cognitive Apprenticeship called the Extreme Apprenticeship method, and the second approach is the use of a massive open online course (MOOC) in programming for recruiting students before they enter their university studies. Furthermore, data from an automatic assessment system implemented for the purposes of this thesis is studied to determine how novices write their first lines of code, and what factors contribute to the feeling of difficulty in learning programming. On average, the teaching approaches described in the literature improve the course pass rates by one third. However, the literature tends to neglect the effect of intervention on the subsequent courses. In both studies at the University of Helsinki, retention improved considerably, and the students on average also fare better in subsequent courses. Finally, the data that has been gathered with the automatic assessment system provides an excellent starting point for future research.Ohjelmointi on nykyajan käsityöläistaito, jolle on akuutti tarve työelämässä. Tämän taidon opettelua harkitseva tietää harvoin, kuinka riippuvainen yhteiskuntamme on ohjelmoinnin tuotoksista eli ohjelmistoista. Ilman ohjelmointia esimerkiksi yhteydenpito, kaupankäynti, matkustaminen ja terveydenhuolto olisivat heikommalla tasolla. Puhelimet eivät toimisi, internettiä ei olisi, eikä lääketeollisuus pystyisi käsittelemään yhtä suuria datamassoja uusia parannuskeinoja etsiessä. Kukaan ei olisi kirjoittanut ohjelmaa, joka auttoi avaruuteen pääsemisessä. Väitöskirjassa tarkastellaan ohjelmoinnin opetusmenetelmiä ja niiden toimivuutta korkeakouluissa sekä esitellään kognitiiviseen oppipoikamalliin (Cognitive Apprenticeship) perustuva “ajatuskäsityöläisten” opetusmenetelmä tehostettu kisällioppiminen (Extreme Apprenticeship). Tehostetussa kisällioppimisessa oppimista edesauttava yksilöllinen ohjaus on mahdollista skaalata satoja opiskelijoita sisältäville kursseille. Väitöskirjatyössä ehdotetaan lisäksi kaikille avoimen verkkokurssin (MOOC) käyttöä yliopisto-opiskelijoiden valintaan sekä tarkastellaan tällaisen valintaväylän toimivuutta tietojenkäsittelytieteen alalla. Väitöskirja käsittelee myös ohjelmointitehtävien automaattista arviointia ja esittelee tähän tarkoitetun Test My Code -järjestelmän, jota voidaan käyttää askeleittaisten ohjeiden ja palautteen antamiseen aloitteleville ohjelmoijille sekä tiedon keräämiseen ohjelmointiprosessissa esiintyvistä ongelmista. Tällaista tietoa voidaan tutkia oppimisanalytiikan menetelmin. Väitöskirjassa tarkastellaan myös aloittelevien ohjelmoijien ensimmäisten ohjelmien kirjoittamisessa esiintyviä ongelmia sekä esitellään ohjelmointitehtävien vaikeuden ennustamiseen sopivia menetelmiä

Automated assessment of programming assignments : visual feedback, assignment mobility, and assessment of students' testing skills

The main objective of this thesis is to improve the automated assessment of programming assignments from the perspective of assessment tool developers. We have developed visual feedback on functionality of students' programs and explored methods to control the level of detail in visual feedback. We have found that visual feedback does not require major changes to existing assessment platforms. Most modern platforms are web based, creating an opportunity to describe visualizations in JavaScript and HTML embedded into textual feedback. Our preliminary results on the effectiveness of automatic visual feedback indicate that students perform equally well with visual and textual feedback. However, visual feedback based on automatically extracted object graphs can take less time to prepare than textual feedback of good quality. We have also developed programming assignments that are easier to port from one server environment to another by performing assessment on the client-side. This not only makes it easier to use the same assignments in different server environments but also removes the need for sandboxing the execution of students' programs. The approach will likely become more important in the future together with interactive study materials becoming more popular. Client-side assessment is more suitable for self-studying material than for grading because assessment results sent by a client are often too easy to falsify. Testing is an important part of programming and automated assessment should also cover students' self-written tests. We have analyzed how students behave when they are rewarded for structural test coverage (e.g. line coverage) and found that this can lead students to write tests with good coverage but with poor ability to detect faulty programs. Mutation analysis, where a large number of (faulty) programs are automatically derived from the program under test, turns out to be an effective way to detect tests otherwise fooling our assessment systems. Applying mutation analysis directly for grading is problematic because some of the derived programs are equivalent with the original and some assignments or solution strategies generate more equivalent mutants than others

Predicting Academic Success Based on Learning Material Usage

In this work, we explore students' usage of online learning material as a predictor of academic success. In the context of an introductory programming course, we recorded the amount of time that each element such as a text paragraph or an image was visible on the students' screen. Then, we applied machine learning methods to study to what extent material usage predicts course outcomes. Our results show that the time spent with each paragraph of the online learning material is a moderate predictor of student success even when corrected for student time-on-task, and that the information can be used to identify at-risk students. The predictive performance of the models is dependent on the quantity of data, and the predictions become more accurate as the course progresses. In a broader context, our results indicate that course material usage can be used to predict academic success, and that such data can be collected in-situ with minimal interference to the students' learning process.Peer reviewe

Introductory programming in higher education: A systematic literature review

A systematic literature review was performed on 33 papers obtained from the ACM, IEEE and Sciencedirect databases, in order to understand in depth, the introductory programming discipline (CS1) in higher education. Recently published works have been covered, providing an overview of the teaching-learning process of introductory programming and enabling to find out whether the research developed by universities worldwide is in line with the proposals made by ACM/IEEE-CS group for computer courses, regarding the transition to the competency-based model. The results show that the new techniques/technologies currently used in software development, as an example of agile methodology, has influenced the teaching-learning process of CS1 together with methods such as visual programming and e-learning. The analyzed papers discuss the importance of developing not only technical, but also social skills, corroborating that methodologies used in introductory programming courses need to focus on preparing students for an increasingly competitive market, associating new skills with technical aspects.This work is supported by CIEd – Research Centre on Education, Institute of Education, University of Minh

Teaching how to program using automated assessment and functional glossy games (Experience Report)

Our department has long been an advocate of the functional-first school of programming and has been teaching Haskell as a first language in introductory programming course units for 20 years. Although the functional style is largely beneficial, it needs to be taught in an enthusiastic and captivating way to fight the unusually high computer science drop-out rates and appeal to a heterogeneous population of students.This paper reports our experience of restructuring, over the last 5 years, an introductory laboratory course unit that trains hands-on functional programming concepts and good software development practices. We have been using game programming to keep students motivated, and following a methodology that hinges on test-driven development and continuous bidirectional feedback. We summarise successes and missteps, and how we have learned from our experience to arrive at a model for comprehensive and interactive functional game programming assignments and a general functionally-powered automated assessment platform, that together provide a more engaging learning experience for students. In our experience, we have been able to teach increasingly more advanced functional programming concepts while improving student engagement.The authors would like to thank the precursors of the 20-year functional programming culture and FPro unit at our university, and all the instructors and TAs that have been involved in the PLab unit throughout the years. This work is financed by the ERDFs European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the Portuguese funding agency, FCT s Fundacao para a Ciencia e a Tecnologia as part of project UID/EEA/50014/2013

Utilizing educational technology in computer science and programming courses : theory and practice

There is one thing the Computer Science Education researchers seem to agree: programming is a difficult skill to learn. Educational technology can potentially solve a number of difficulties associated with programming and computer science education by automating assessment, providing immediate feedback and by gamifying the learning process. Still, there are two very important issues to solve regarding the use of technology: what tools to use, and how to apply them? In this thesis, I present a model for successfully adapting educational technology to computer science and programming courses. The model is based on several years of studies conducted while developing and utilizing an exercise-based educational tool in various courses. The focus of the model is in improving student performance, measured by two easily quantifiable factors: the pass rate of the course and the average grade obtained from the course. The final model consists of five features that need to be considered in order to adapt technology effectively into a computer science course: active learning and continuous assessment, heterogeneous exercise types, electronic examination, tutorial-based learning, and continuous feedback cycle. Additionally, I recommend that student mentoring is provided and cognitive load of adapting the tools considered when applying the model. The features are classified as core components, supportive components or evaluation components based on their role in the complete model. Based on the results, it seems that adapting the complete model can increase the pass rate statistically significantly and provide higher grades when compared with a “traditional” programming course. The results also indicate that although adapting the model partially can create some improvements to the performance, all features are required for the full effect to take place. Naturally, there are some limits in the model. First, I do not consider it as the only possible model for adapting educational technology into programming or computer science courses. Second, there are various other factors in addition to students’ performance for creating a satisfying learning experience that need to be considered when refactoring courses. Still, the model presented can provide significantly better results, and as such, it works as a base for future improvements in computer science education.Ohjelmoinnin oppimisen vaikeus on yksi harvoja asioita, joista lähes kaikki tietojenkäsittelyn opetuksen tutkijat ovat jokseenkin yksimielisiä. Opetusteknologian avulla on mahdollista ratkaista useita ohjelmoinnin oppimiseen liittyviä ongelmia esimerkiksi hyödyntämällä automaattista arviointia, välitöntä palautetta ja pelillisyyttä. Teknologiaan liittyy kuitenkin kaksi olennaista kysymystä: mitä työkaluja käyttää ja miten ottaa ne kursseilla tehokkaasti käyttöön? Tässä väitöskirjassa esitellään malli opetusteknologian tehokkaaseen hyödyntämiseen tietojenkäsittelyn ja ohjelmoinnin kursseilla. Malli perustuu tehtäväpohjaisen oppimisjärjestelmän runsaan vuosikymmenen pituiseen kehitys- ja tutkimusprosessiin. Mallin painopiste on opiskelijoiden suoriutumisen parantamisessa. Tätä arvioidaan kahdella kvantitatiivisella mittarilla: kurssin läpäisyprosentilla ja arvosanojen keskiarvolla. Malli koostuu viidestä tekijästä, jotka on otettava huomioon tuotaessa opetusteknologiaa ohjelmoinnin kursseille. Näitä ovat aktiivinen oppiminen ja jatkuva arviointi, heterogeeniset tehtävätyypit, sähköinen tentti, tutoriaalipohjainen oppiminen sekä jatkuva palautesykli. Lisäksi opiskelijamentoroinnin järjestäminen kursseilla ja järjestelmän käyttöönottoon liittyvän kognitiivisen kuorman arviointi tukevat mallin käyttöä. Malliin liittyvät tekijät on tässä työssä lajiteltu kolmeen kategoriaan: ydinkomponentteihin, tukikomponentteihin ja arviontiin liittyviin komponentteihin. Tulosten perusteella vaikuttaa siltä, että mallin käyttöönotto parantaa kurssien läpäisyprosenttia tilastollisesti merkittävästi ja nostaa arvosanojen keskiarvoa ”perinteiseen” kurssimalliin verrattuna. Vaikka mallin yksittäistenkin ominaisuuksien käyttöönotto voi sinällään parantaa kurssin tuloksia, väitöskirjaan kuuluvien tutkimusten perusteella näyttää siltä, että parhaat tulokset saavutetaan ottamalla malli käyttöön kokonaisuudessaan. On selvää, että malli ei ratkaise kaikkia opetusteknologian käyttöönottoon liittyviä kysymyksiä. Ensinnäkään esitetyn mallin ei ole tarkoituskaan olla ainoa mahdollinen tapa hyödyntää opetusteknologiaa ohjelmoinnin ja tietojenkäsittelyn kursseilla. Toiseksi tyydyttävään oppimiskokemukseen liittyy opiskelijoiden suoriutumisen lisäksi paljon muitakin tekijöitä, jotka tulee huomioida kurssien uudelleensuunnittelussa. Esitetty malli mahdollistaa kuitenkin merkittävästi parempien tulosten saavuttamisen kursseilla ja tarjoaa sellaisena perustan entistä parempaan opetukseen

Predicting programming assignment difficulty

Teaching programming is increasingly more widespread and starts at primary school level on some countries. Part of that teaching consist of students writing small programs that will demonstrate learned theory and how various things fit together to form a functional program. Multiple studies indicate that programming is difficult skill to learn and master. Some part of difficulty comes from plethora of concepts that students are expected to learn in relatively short time. Part of practicing to write programs involves feedback, which aids students’ learning of assignment’s topic, and motivation, which encourages students to continue the course and their studies. For feedback it would be helpful to know students’ opinion of a programming assignment difficulty. There are few studies that attempt to find out if there is correlation between metrics that are obtained from students’ writing a program and their reported difficulty of it. These studies use statistical models on data after the course is over. This leads to an idea if such a thing could be done while students are working on programming assignments. To do this some sort of machine learning model would be possible solution but as of now no such models exist. Due to this we will utilize idea from one of these studies to create a model, which could do such prediction. We then improve that model, which is coarse, with two additional models that are more tailored for the job. Our main results indicate that this kind of models show promise in their prediction of a programming assignment difficulty based on collected metrics. With further work these models could provide indication of a student struggling on some assignment. Using this kind of model as part of existing tools we could provide a student subtle help before his frustration grows too much. Further down the road such a model could be used to provide further exercises, if need by a student, or progress forward once he masters certain topic