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

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

Integrating algorithm visualization video into a first-year algorithm and data structure course

In this paper we describe the results that we have obtained while integrating algorithm visualization (AV) movies (strongly tightened with the other teaching material), within a first-year undergraduate course on algorithms and data structures. Our experimental results seem to support the hypothesis that making these movies available significantly improved students' performances. Moreover, the movies were highly appreciated by the students (both from a comprehensibility point of view and from a usefulness point of view), even though with a low attitude towards the emerging video pod-cast technology. Finally, our results indicate the necessity of integrating the AV movies with audio comment, which seems to be one of the most interesting research question left open by our study. © International Forum of Educational Technology & Society (IFETS)

Social Worked-Examples Technique to Enhance Student Engagement in Program Visualization

يعد تعلم البرمجة من بين أهم التحديات في تعليم علوم الكمبيوتر. حاليا، يتم استخدام تصوير البرامج ) PV ( كأداة للتغلب علىمعدلات الفشل والتسرب العالية في مادة اساسيات البرمجة. ومع ذلك، هناك مخاوف متزايدة بشأن فعالية أدوات تصوير البرامج الحالية استناداالى النتائج المختلطة المستمدة من الدراسات المختلفة. تعتبر مشاركة الطلاب أيضًا عاملاً حيويًا في بناء PV ناجحًا، كما تعد أيضًا جزءًا مهمًامن عملية التعلم بشكل عام. تم إدخال العديد من التقنيات لتعزيز المشاركة في أدوات تصوير البرامج؛ ومع ذلك، فإن مشاركة الطلاب في PVلا يزال يمثل تحديًا كبيراً. استخدمت هذه الورقة ثلاث نظريات مختلفة: البنيوية، والبناء الاجتماعي، والحمل المعرفي لاقتراح تقنية لتعزيزمشاركة الطلاب في استخدام أدوات تصوير البرامج. تعمل تقنية الأمثلة المكتملة الاجتماعية ) SWE ( على تحويل المثال المكتمل التقليدي إلىنشاط اجتماعي ، حيث يتم التركيز بشكل أكبر على دور التعاون في بناء معرفة الطلاب. حددت هذه الدراسة ثلاثة مبادئ يمكن أن تعززمشاركة الطلاب من خلال تقنية SWE : التعلم النشط والتعاون الاجتماعي والأنشطة ذاتس التحميل المنخفض.Learning programming is among the top challenges in computer science education. A part of that, program visualization (PV) is used as a tool to overcome the high failure and drop-out rates in an introductory programming course. Nevertheless, there are rising concerns about the effectiveness of the existing PV tools following the mixed results derived from various studies. Student engagement is also considered a vital factor in building a successful PV, while it is also an important part of the learning process in general. Several techniques have been introduced to enhance PV engagement; however, student engagement with PV is still challenging. This paper employed three theories—constructivism, social constructivism and cognitive load to propose a technique for enhancing student engagement with program visualisation. The social worked-examples (SWE) technique transforms the traditional worked-example into a social activity, whereby a greater focus is placed on the collaboration role in constructing students’ knowledge. This study identified three principles that could enhance student engagement through the SWE technique: active learning, social collaboration and low-load activity

Personalized learning paths based on Wikipedia article statistics

We propose a new semi-automated method for generating personalized learning paths from the Wikipediaonline encyclopedia by following inter-article hyperlink chains based on various rankings that are retrieved from the statistics of the articles. Alternative perspectives for learning topics are achieved when the next hyperlink to access is selected based on hierarchy of hyperlinks, repetition of hyperlink terms, article size, viewing rate, editing rate, or user-defined weighted mixture of them all. We have implemented the method in a prototype enabling the learner to build independently concept maps following her needs and consideration. A list of related concepts is shown in a desired type of ranking to label new nodes (titles of target articles for current hyperlinks) accompanied with parsed explanation phrases from the sentences surrounding each hyperlink to label directed arcs connecting nodes. In experiments the alternative ranking schemes well supported various learning needs suggesting new pedagogical networking practices.Peer reviewe

INVESTIGATING THE IMPACT OF ONLINE HUMAN COLLABORATION IN EXPLANATION OF AI SYSTEMS

An important subdomain in research on Human-Artificial Intelligence interaction is Explainable AI (XAI). XAI aims to improve human understanding and trust in machine intelligence and automation by providing users with visualizations and other information explaining the AI’s decisions, actions, or plans and thereby to establish justified trust and reliance. XAI systems have primarily used algorithmic approaches designed to generate explanations automatically that help understanding underlying information about decisions and establish justified trust and reliance, but an alternate that may augment these systems is to take advantage of the fact that user understanding of AI systems often develops through self-explanation (Mueller et al., 2021). Users attempt to piece together different sources of information and develop a clearer understanding, but these self-explanations are often lost if not shared with others. This thesis research demonstrated how this ‘Self-Explanation’ could be shared collaboratively via a system that is called collaborative XAI (CXAI). It is akin to a Social Q&A platform (Oh, 2018) such as StackExchange. A web-based system was built and evaluated formatively and via user studies. Formative evaluation will show how explanations in an XAI system, especially collaborative explanations, can be assessed based on ‘goodness criteria’ (Mueller et al., 2019). This thesis also investigated how the users performed with the explanations from this type of XAI system. Lastly, the research investigated whether the users of CXAI system are satisfied with the human-generated explanations generated in the system and check if the users can trust this type of explanation