Social Addictive Gameful Engineering (SAGE): A Game-based Learning and Assessment System for Computational Thinking

At an unrivaled and enduring pace, computing has transformed the world, resulting in demand for a universal fourth foundation beyond reading, writing, and arithmetic: computational thinking (CT). Despite increasingly widespread acceptance of CT as a crucial competency for all, transforming education systems accordingly has proven complex. The principal hypothesis of this thesis is that we can improve the efficiency and efficacy of teaching and learning CT by building gameful learning and assessment systems on top of block-based programming environments. Additionally, we believe this can be accomplished at scale and cost conducive to accelerating CT dissemination for all. After introducing the requirements, approach, and architecture, we present a solution named Gameful Direct Instruction. This involves embedding Parsons Programming Puzzles (PPPs) in Scratch, which is a block-based programming environment currently used prevalently in grades 6-8. PPPs encourage students to practice CT by assembling into correct order sets of mixed-up blocks that comprise samples of well-written code which focus on individual concepts. The structure provided by PPPs enable instructors to design games that steer learner attention toward targeted learning goals through puzzle-solving play. Learners receive continuous automated feedback as they attempt to arrange programming constructs in correct order, leading to more efficient comprehension of core CT concepts than they might otherwise attain through less structured Scratch assignments. We measure this efficiency first via a pilot study conducted after the initial integration of PPPs with Scratch, and second after the addition of scaffolding enhancements in a study involving a larger adult general population. We complement Gameful Direct Instruction with a solution named Gameful Constructionism. This involves integrating with Scratch implicit assessment functionality that facilitates constructionist video game (CVG) design and play. CVGs enable learner to explore CT using construction tools sufficiently expressive for personally meaningful gameplay. Instructors are enabled to guide learning by defining game objectives useful for implicit assessment, while affording learners the opportunity to take ownership of the experience and progress through the sequence of interest and motivation toward sustained engagement. When strategically arranged within a learning progression after PPP gameplay produces evidence of efficient comprehension, CVGs amplify the impact of direct instruction by providing the sculpted context in which learners can apply CT concepts more freely, thereby broadening and deepening understanding, and improving learning efficacy. We measure this efficacy in a study of the general adult population. Since these approaches leverage low fidelity yet motivating gameful techniques, they facilitate the development of learning content at scale and cost supportive of widespread CT uptake. We conclude this thesis with a glance at future work that anticipates further progress in scalability via a solution named Gameful Intelligent Tutoring. This involves augmenting Scratch with Intelligent Tutoring System (ITS) functionality that offers across-activity next-game recommendations, and within-activity just-in-time and on-demand hints. Since these data-driven methods operate without requiring knowledge engineering for each game designed, the instructor can evolve her role from one focused on knowledge transfer to one centered on supporting learning through the design of educational experiences, and we can accelerate the dissemination of CT at scale and reasonable cost while also advancing toward continuously differentiated instruction for each learner

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

Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory

This thesis develops a theoretical framework for the generation of artificial societies. In particular it shows how sub-systems emerge when the agents are able to learn and have the ability to communicate. This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated originally by the German sociologist Luhmann, with concepts of Shannon's information theory applied to adaptive learning agents. Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational modelling paradigm involving the modelling of phenomena as dynamical systems of interacting agents. The thesis in particular, investigates the functions and properties necessary to reproduce the paradigm of society by using the mentioned ABM approach. Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems can then be composed by agents with a reduced behavioural complexity. For example in society there are people who produce goods and other who distribute them. Both the behaviour and communication is learned by the agent and not imposed. The simulated task is to collect food, keep it and eat it until sated. Every agent communicates its energy state to the neighbouring agents. This results in two subsystems whereas agents in the first collect food and in the latter steal food from others. The ratio between the number of agents that belongs to the first system and to the second system, depends on the number of food resources. Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise by reducing the amount of sensory information or, equivalently, reducing the complexity of the perceived environment from the agent's perspective. Shannon's information theorem is used to assess the performance of the simulated learning agents. A practical measure, based on the concept of Shannon's information ow, is developed and applied to adaptive controllers which use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning. The behavioural complexity is measured with a novel information measure, called Predictive Performance, which is able to measure at a subjective level how good an agent is performing a task. This is then used to quantify the social division of tasks in a social group of honest, cooperative food foraging, communicating agents

Challenges for engineering students working with authentic complex problems

Engineers are important participants in solving societal, environmental and technical problems. However, due to an increasing complexity in relation to these problems new interdisciplinary competences are needed in engineering. Instead of students working with monodisciplinary problems, a situation where students work with authentic complex problems in interdisciplinary teams together with a company may scaffold development of new competences. The question is: What are the challenges for students structuring the work on authentic interdisciplinary problems? This study explores a three-day event where 7 students from Aalborg University (AAU) from four different faculties and one student from University College North Denmark (UCN), (6th-10th semester), worked in two groups at a large Danish company, solving authentic complex problems. The event was structured as a Hackathon where the students for three days worked with problem identification, problem analysis and finalizing with a pitch competition presenting their findings. During the event the students had workshops to support the work and they had the opportunity to use employees from the company as facilitators. It was an extracurricular activity during the summer holiday season. The methodology used for data collection was qualitative both in terms of observations and participants’ reflection reports. The students were observed during the whole event. Findings from this part of a larger study indicated, that students experience inability to transfer and transform project competences from their previous disciplinary experiences to an interdisciplinary setting

Exploring the practical use of a collaborative robot for academic purposes

This article presents a set of experiences related to the setup and exploration of potential educational uses of a collaborative robot (cobot). The basic principles that have guided the work carried out have been three. First and foremost, study of all the functionalities offered by the robot and exploration of its potential academic uses both in subjects focused on industrial robotics and in subjects of related disciplines (automation, communications, computer vision). Second, achieve the total integration of the cobot at the laboratory, seeking not only independent uses of it but also seeking for applications (laboratory practices) in which the cobot interacts with some of the other devices already existing at the laboratory (other industrial robots and a flexible manufacturing system). Third, reuse of some available components and minimization of the number and associated cost of required new components. The experiences, carried out following a project-based learning methodology under the framework of bachelor and master subjects and thesis, have focused on the integration of mechanical, electronic and programming aspects in new design solutions (end effector, cooperative workspace, artificial vision system integration) and case studies (advanced task programming, cybersecure communication, remote access). These experiences have consolidated the students' acquisition of skills in the transition to professional life by having the close collaboration of the university faculty with the experts of the robotics company.Postprint (published version