Gamification in higher education and stem : a systematic review of literature

In recent years, gamification, the use of game elements in non-game contexts, has drawn the attention of educators due to the possibility of making learning more motivating and engaging; this led to an increase of research in the field. Despite the availability of literature reviews about gamification and its effects, no work to this date has focused exclusively on Higher Education (HE). Next, worldwide there is an increasing demand for skilled Science, Technology, Engineering and Mathematics (STEM) professionals that meet the challenges related to scientific and technological innovations of the 21st Century. This lead to the need of strengthening STEM Higher Education. This brings us to the purpose of this work: presenting a systematic literature review of empirical studies about gamification STEM related Higher Education. This review study started from a systematic mapping design of 'Web of Science' articles, with following inclusion criteria: empirical gamification studies set up in HE, published between 2000 and 2016; focusing on undergraduate or graduate students; in the STEM knowledge field, and set up in authentic settings. An initial search resulted in 562 potentially relevant articles. After applying all selection criteria, only 18 studies could be retained. 12 additional articles were included by analyzing references from earlier literature reviews, resulting in 30 studies to be included. Analysis results show how a combination of game elements (e.g. leaderboards, badges, points and other combinations) positively affects students' performance, attendance, goal orientation and attitude towards mostly computer science related subjects. The analysis results also point at a lack of studies in certain STEM areas, a lack of studies that identify the particular game element associated with the positive differential impact on student performance; a lack of validated psychometric measurements, and lack of focus on student variables that could/should be taken into account as mediating/moderating variables clarifying the impact of gamification in the HE focus on STEM learning and teaching

Analyzing the behavior of students regarding learning activities, badges, and academic dishonesty in MOOC environment

Mención Internacional en el título de doctorThe ‘big data’ scene has brought new improvement opportunities to most products and services, including education. Web-based learning has become very widespread over the last decade, which in conjunction with the Massive Open Online Course (MOOC) phenomenon, it has enabled the collection of large and rich data samples regarding the interaction of students with these educational online environments. We have detected different areas in the literature that still need improvement and more research studies. Particularly, in the context of MOOCs and Small Private Online Courses (SPOCs), where we focus our data analysis on the platforms Khan Academy, Open edX and Coursera. More specifically, we are going to work towards learning analytics visualization dashboards, carrying out an evaluation of these visual analytics tools. Additionally, we will delve into the activity and behavior of students with regular and optional activities, badges and their online academically dishonest conduct. The analysis of activity and behavior of students is divided first in exploratory analysis providing descriptive and inferential statistics, like correlations and group comparisons, as well as numerous visualizations that facilitate conveying understandable information. Second, we apply clustering analysis to find different profiles of students for different purposes e.g., to analyze potential adaptation of learning experiences and pedagogical implications. Third, we also provide three machine learning models, two of them to predict learning outcomes (learning gains and certificate accomplishment) and one to classify submissions as illicit or not. We also use these models to discuss about the importance of variables. Finally, we discuss our results in terms of the motivation of students, student profiling, instructional design, potential actuators and the evaluation of visual analytics dashboards providing different recommendations to improve future educational experiments.Las novedades en torno al ‘big data’ han traído nuevas oportunidades de mejorar la mayoría de productos y servicios, incluyendo la educación. El aprendizaje mediante tecnologías web se ha extendido mucho durante la última década, que conjuntamente con el fenómeno de los cursos abiertos masivos en línea (MOOCs), ha permitido que se recojan grandes y ricas muestras de datos sobre la interacción de los estudiantes con estos entornos virtuales de aprendizaje. Nosotros hemos detectado diferentes áreas en la literatura que aún necesitan de mejoras y del desarrollo de más estudios, específicamente en el contexto de MOOCs y cursos privados pequeños en línea (SPOCs). En la tesis nos hemos enfocado en el análisis de datos en las plataformas Khan Academy, Open edX y Coursera. Más específicamente, vamos a trabajar en interfaces de visualizaciones de analítica de aprendizaje, llevando a cabo la evaluación de estas herramientas de analítica visual. Además, profundizaremos en la actividad y el comportamiento de los estudiantes con actividades comunes y opcionales, medallas y sus conductas en torno a la deshonestidad académica. Este análisis de actividad y comportamiento comienza primero con análisis exploratorio proporcionando variables descriptivas y de inferencia estadística, como correlaciones y comparaciones entre grupos, así como numerosas visualizaciones que facilitan la transmisión de información inteligible. En segundo lugar aplicaremos técnicas de agrupamiento para encontrar distintos perfiles de estudiantes con diferentes propósitos, como por ejemplo para analizar posibles adaptaciones de experiencias educativas y sus implicaciones pedagógicas. También proporcionamos tres modelos de aprendizaje máquina, dos de ellos que predicen resultados finales de aprendizaje (ganancias de aprendizaje y la consecución de certificados de terminación) y uno para clasificar que ejercicios han sido entregados de forma deshonesta. También usaremos estos tres modelos para analizar la importancia de las variables. Finalmente, discutimos todos los resultados en términos de la motivación de los estudiantes, diferentes perfiles de estudiante, diseño instruccional, posibles sistemas actuadores, así como la evaluación de interfaces de analítica visual, proporcionando recomendaciones que pueden ayudar a mejorar futuras experiencias educacionales.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Davinia Hernández Leo.- Secretario: Luis Sánchez Fernández.- Vocal: Adolfo Ruiz Callej

Supporting Self-Regulated Learning with Visualizations in Online Learning Environments

In this article, we study how visualizations could be used to support students' self-regulation in online learning. We conducted a randomized controlled trial with three groups: one control group without visualization, one treatment group with textual visualization, and one treatment with graphical visualization with information on peers' average achievement. We studied how different visualizations affect students' academic performance and behavior. We focused on four factors; starting, scheduling, earliness and exercise points, where the first three are related to time management and self-regulation. The last factor measures course performance in terms of completed exercises. Our results suggest that the lowest performing students can benefit from a visualization, whereas the highest performing students are not affected by the presence or absence of a visualization. We also found that visualizations that do not provide the means to compare your own performance with others may even be harmful to performance oriented students.Peer reviewe

Tavoiteorientaatioprofiilit ja suoriutuminen ohjelmoinnin MOOC-kurssilla

Tavoitteet. Valtaosa tietojenkäsittelytieteen kontekstissa tehdystä tavoiteorientaatiotutkimuksesta on ollut muuttujalähtöistä. Tämän tutkielman tavoitteena oli syventää ymmärrystä tietojenkäsittelytieteen opiskelijoista ja saavutusmotivaatiosta henkilösuuntautunutta lähestymistapaa käyttäen. Eri tavoiteorientaatioiden välistä vuorovaikutusta tarkasteltiin tunnistamalla yleisimmät tavoiteorientaatioprofiilit ja tutkimalla niiden välisiä eroja suoriutumisessa. Toisin kuin aiemmissa henkilösuuntautunutta lähestymistapaa hyödyntävissä tutkimuksissa, ryhmittely-muuttujina käytettiin oppimisorientaation lisäksi suoritusorientaatiota jaoteltuna tarkemmin tavoitteisiin päihittää toiset (normative goal) ja vaikuttaa pätevältä (appearance goal). Menetelmät. Tutkimukseen osallistui 2059 avoimen internet-pohjaisen ohjelmoinnin alkeiskurssin opiskelijaa. Aineisto kerättiin kyselylomakkeella, automaattisesti arvioiduista ohjelmointitehtävistä ja loppukokeesta. Tavoiteorientaatiomittarin rakennetta tarkasteltiin eksploratiivisella faktorianalyysillä (EFA). Opiskelijat luokiteltiin ryhmiin tavoiteorientaatioiden perusteella TwoStep-klusterianalyysia käyttäen. Profiilien ominaispiirteitä ja eroja suoriutumisessa tutkittiin ristiintaulukointien ja varianssianalyysien (ANOVA) avulla. Tulokset ja johtopäätökset. Tavoiteorientaatioprofiileja tunnistettiin viisi: Saavutusorientoituneet (31,2%), Suoritusorientoituneet (18,9%), Oppimis- ja suoritusorientoituneet (18,0%), Vähäisesti motivoituneet (17,6%) ja Oppimisorientoituneet (14,3%). Oppimis- ja suoritusorientoituneiden opiskelijoiden suoriutuminen oli kahden mittarin osalta tilastollisesti merkitsevästi parempaa kuin Vähäisesti motivoituneiden opiskelijoiden. Aiempien tutkimusten tapaan tuloksissa korostuu useampaan tavoitteeseen pyrkimisen ja suoriutumisen välinen positiivinen yhteys. Lisää tutkimusta tarvitaan tavoiteorientaatioprofiilien ja muiden koulutukseen liittyvien tulosten yhteyksien selvittämiseen ohjelmoinnin opetuksen kontekstissa. Tämänkaltaista tietoa voidaan hyödyntää uusia oppimisinterventioita ja kursseja suunniteltaessa. Tähän tutkielmaan perustuva artikkeli ‘Achievement Goal Orientation Profiles and Performance in a Programming MOOC’ tullaan esittelemään ITiCSE 2020 -konferenssissa ja julkaisemaan konferenssijulkaisussa.Aims. In the context of computing education, the vast majority of prior research examining achievement goal orientations has been conducted using variable-centred methods. In order to deepen understanding of the student population and achievement motivation, this Master’s Thesis employed person-oriented perspectives. The interplay of different goal orientations was explored by identifying prevalent motivational profiles and investigating profile differences in performance. Normative and appearance performance goals were handled as separate clustering variables in addition to mastery goals for the first time. Methods. The participants were 2059 introductory programming MOOC students. Data were collected by a questionnaire and from automatically assessed programming assignments and final exam. An exploratory factor analysis (EFA) was conducted for the achievement goal orientation items to examine the factor structure. Using TwoStep cluster analysis, the students were classified into clusters according to their achievement goal orientations. Cross tabulations and analyses of variance (ANOVA) were conducted to investigate profile characteristics and differences in performance. Results and Conclusions. Five distinct achievement goal orientation profiles were identified: Approach-Oriented (31.2%), Performance-Oriented (18.9%), Combined Mastery and Performance Goals (18.0%), Low Goals (17.6.%) and Mastery-Oriented (14.3.%). Students with Combined Mastery and Performance Goals performed significantly better than students with Low Goals regarding two metrics. Consistent with previous findings, the results highlight the positive link between multiple goal pursuit and performance. Further studies are needed to investigate motivational profiles in relation to other educational outcomes in the context of computing education. This kind of knowledge is valuable for designing interventions and new courses. The article ‘Achievement Goal Orientation Profiles and Performance in a Programming MOOC’, which is based on the present thesis, will be presented at ITiCSE 2020 (Conference on Innovation and Technology in Computer Science Education) conference and published in conference proceedings

Investigating the Role of Student Ownership in the Design of Student-facing Learning Analytics Dashboards (SFLADs) in Relation to Student Perceptions of SFLADs

abstract: Learning analytics application is evolving into a student-facing solution. Student-facing learning analytics dashboards (SFLADs), as one popular application, occupies a pivotal position in online learning. However, the application of SFLADs faces challenges due to teacher-centered and researcher-centered approaches. The majority of SFLADs report student learning data to teachers, administrators, and researchers without direct student involvement in the design of SFLADs. The primary design criteria of SFLADs is developing interactive and user-friendly interfaces or sophisticated algorithms that analyze the collected data about students’ learning activities in various online environments. However, if students are not using these tools, then analytics about students are not useful. In response to this challenge, this study focuses on investigating student perceptions regarding the design of SFLADs aimed at providing ownership over learning. The study adopts an approach to design-based research (DBR; Barab, 2014) called the Integrative Learning Design Framework (ILDF; Bannan-Ritland, 2003). The theoretical conjectures and the definition of student ownership are both framed by Self-determination theory (SDT), including four concepts of academic motivation. There are two parts of the design in this study, including prototypes design and intervention design. They are guided by a general theory-based inference which is student ownership will improve student perceptions of learning in an autonomy-supportive SFLAD context. A semi-structured interview is used to gather student perceptions regarding the design of SFLADs aimed at providing ownership over learning.Dissertation/ThesisMasters Thesis Educational Psychology 201

BinCam:designing for engagement with Facebook for behavior change

Abstract. In this paper we continue work to investigate how we can engage young adults in behaviors of recycling and the prevention of food waste through social media and persuasive and ubiquitous computing systems. Our previous work with BinCam, a two-part design combining a system for the collection of waste-related behaviors with a Facebook application, suggested that although this ubiquitous system could raise awareness of recycling behavior, engagement with social media remained low. In this paper we reconsider our design in terms of engagement, examining both the theoretical and practical ways in which engagement can be designed for. This paper presents findings from a new user study exploring the redesign of the social media interface following this analysis. By incorporating elements of gamification, social support and improved data visualization, we contribute insights on the relative potential of these techniques to engage individuals across the lifespan of a system’s deployment

Knowledge Communities in Online Education and (Visual) Knowledge Management: 19. Workshop GeNeMe‘16 as part of IFKAD 2016: Proceedings of 19th Conference GeNeMe

Communities in New Media started in 1998 as a workshop series at TU Dresden, and since then has annually dealt with online communities at the interface between several disciplines such as education and economics, computer science, social and communication sciences, and more. (See Köhler, Kahnwald & Schoop, 2015). The workshop is traditionally a forum for interdisciplinary dialogue between science and business and serves to share experiences and knowledge among participants from different disciplines, organisations, and institutions. In addition to the core themes of knowledge management and communities (in the chapters of the same name), the main focus of the conference is also on the support of knowledge and learning processes in the field of (media-assisted) higher education. This is complemented by an informational perspective when it comes to more functional and methodological approaches - use cases, workflows, and automation in knowledge management. In addition, systems and approaches for feedback, exchange, and ideas are presented. With the focus of knowledge media design and visual research as well as creative processes, this time there is also a highlight on visual aspects of knowledge management and mediation. For IFKAD 2016, three GeNeMe tracks were accepted which focus on the interface of knowledge communities and knowledge management as well as knowledge media design in science, business, or education. In this conference volume you will find detailed information about these three tracks: -- Knowledge Communities I: Knowledge Management -- Knowledge Communities II: Online Education -- Visual Knowledge Management [From the Preface.]:Preface IX Vorwort XIII Knowledge Communities I: Knowledge Management 1 Process Learning Environments 1 Two Steps to IT Transparency: A Practitioner’s Approach for a Knowledge Based Analysis of Existing IT Landscapes in SME 13 Social Media and Sustainable Communication. Rethinking the Role of Research and Innovation Networks 26 Consolidating eLearning in a Higher Education Institution: An Organisational Issue integrating Didactics, Technology, and People by the Means of an eLearning Strategy 39 How to treat the troll? An empirical analysis of counterproductive online behavior, personality traits and organizational behavior 51 Knowledge Communities II: Online Education 64 Sifa-Portfolio – a Continuing Education Concept for Specialists on Industrial Safety Combining Formal and Informal Learning 64 Analysing eCollaboration: Prioritisation of Monitoring Criteria for Learning Analytics in the Virtual Classroom 78 Gamifying Higher Education. Beyond Badges, Points and Leaderboards 93 Virtual International Learning Experience in Formal Higher Education – A Case Study from Jordan 105 Migration to the Flipped Classroom – Applying a Scalable Flipped Classroom Arrangement 117 MOOC@TU9 – Common MOOC Strategy of the Alliance of Nine Leading German Institutes of Technology 131 A Survey on Knowledge Management in Universities in the QS Rankings: E-learning and MOOCs 144 Visual Knowledge Media 157 Generating implications for design in practice: How different stimuli are retrieved and transformed to generate ideas 157 Behind the data – preservation of the knowledge in CH Visualisations 170 Building a Wiki resource on digital 3D reconstruction related knowledge assets 184 Visual media as a tool to acquire soft skills — cross-disciplinary teaching-learning project SUFUvet 196 Graphing Meeting Records - An Approach to Visualize Information in a Multi Meeting Context 209 HistStadt4D – A four dimensional access to history 221 Ideagrams: A digital tool for observing ideation processes 234 Adress- und Autorenverzeichnis 251Gemeinschaften in Neuen Medien hat 1998 als Workshop-Reihe an der TU Dresden begonnen und seither jährlich das Thema Online-Communities an der Schnittstelle mehrerer Disziplinen wie Informatik, Bildungs- und Wirtschaftswissenschaften, Informatik sowie Sozial-und Kommunikationswissenschaft u.a.m. thematisiert (vgl. Köhler, Kahnwald & Schoop, 2015). Der Workshop ist traditionell ein Forum für den interdisziplinären Dialog zwischen Wissenschaft und Wirtschaft und dient dazu, Erfahrungen und Wissen unter den Teilnehmern aus verschiedenen Disziplinen, Organisationen und Institutionen zu teilen. Die inhaltlichen Schwerpunkte der Konferenz widmen sich neben den Kernthemen Wissensmanagement und Communities (in den gleichnamigen Kapiteln) auch der Unterstützung von Wissens- und Lernprozessen im Bereich der (mediengestützten) Hochschullehre. Ergänzt wird diese eher organisationswissenschaftliche durch eine informatorische Perspektive, wenn es um stärker funktionale bzw. auch methodische Ansätze geht – Use Cases, Workflows und Automatisierung im Wissensmanagement. Darüber hinaus werden Systeme und Ansätze für Feedback, Austausch und Ideenfindung vorgestellt. Mit den Schwerpunkten der Wissensmediengestaltung und visuellen Forschungs- sowie Kreativprozessen wird diesmal auch ein Schlaglicht auf visuelle Aspekte von Wissensmanagement und -vermittlung geworfen. Für die IFKAD 2016 wurden drei GeNeMe-Tracks angenommen, die sich auf das Interface von Wissensgemeinschaften und Wissensmanagement sowie die Wissensmediengestaltung in Wissenschaft, Wirtschaft oder Bildung konzentrieren. Im vorliegenden Tagungsband finden Sie detaillierte Informationen zu diesen drei Tracks: -- Knowledge Communities I: Knowledge Management -- Knowledge Communities II: Online Education -- Visual Knowledge Management [Aus dem Vorwort.]:Preface IX Vorwort XIII Knowledge Communities I: Knowledge Management 1 Process Learning Environments 1 Two Steps to IT Transparency: A Practitioner’s Approach for a Knowledge Based Analysis of Existing IT Landscapes in SME 13 Social Media and Sustainable Communication. Rethinking the Role of Research and Innovation Networks 26 Consolidating eLearning in a Higher Education Institution: An Organisational Issue integrating Didactics, Technology, and People by the Means of an eLearning Strategy 39 How to treat the troll? An empirical analysis of counterproductive online behavior, personality traits and organizational behavior 51 Knowledge Communities II: Online Education 64 Sifa-Portfolio – a Continuing Education Concept for Specialists on Industrial Safety Combining Formal and Informal Learning 64 Analysing eCollaboration: Prioritisation of Monitoring Criteria for Learning Analytics in the Virtual Classroom 78 Gamifying Higher Education. Beyond Badges, Points and Leaderboards 93 Virtual International Learning Experience in Formal Higher Education – A Case Study from Jordan 105 Migration to the Flipped Classroom – Applying a Scalable Flipped Classroom Arrangement 117 MOOC@TU9 – Common MOOC Strategy of the Alliance of Nine Leading German Institutes of Technology 131 A Survey on Knowledge Management in Universities in the QS Rankings: E-learning and MOOCs 144 Visual Knowledge Media 157 Generating implications for design in practice: How different stimuli are retrieved and transformed to generate ideas 157 Behind the data – preservation of the knowledge in CH Visualisations 170 Building a Wiki resource on digital 3D reconstruction related knowledge assets 184 Visual media as a tool to acquire soft skills — cross-disciplinary teaching-learning project SUFUvet 196 Graphing Meeting Records - An Approach to Visualize Information in a Multi Meeting Context 209 HistStadt4D – A four dimensional access to history 221 Ideagrams: A digital tool for observing ideation processes 234 Adress- und Autorenverzeichnis 25

Students’ experiences of learning analytics in academic advising for supporting self-regulated learning

Abstract. This qualitative thesis was located at the intersection between learning analytics and self-regulated learning where academic advising worked as a context. The examination was limited to self-regulation of behavior and further to three resource management strategies: time management, effort regulation and help seeking. Also, the examination of learning analytics was limited to visualizations developed in a research project called AnalyticsAI. Even though the importance of involving students’ perspectives to the development processes of learning analytics applications is well acknowledged, there are currently only few studies regarding it. The main goal of this thesis was to contribute by addressing this gap in previous research by providing insights how self-regulated learning can be supported via learning analytics according to students themselves. More precisely I was interested in finding answers to three research questions regarding students’ own challenges and needs for support concerning resource management strategies and progress in studies, students’ experiences of the visualizations under development and students’ expectations for their further development. Participants were ten students from the University of Oulu who attended the pilot study conducted in AnalyticsAI in the academic year 2019–2020. The data of this thesis was collected through semi-structured interviews with stimulated recall method, and was analyzed with qualitative theory directed content analysis in which transcriptions of interviews worked as research material. The results indicated that students in this study were well-achieving and reported only minor challenges and needs for support which generally had not affected their progress in studies. Students also had different preferences regarding the current visualizations and their use in advising context which appeared as mixed experiences. Generally students experienced that visualizations make needs for support more visible and therefore they were perceived to be especially useful for students with more challenges. Students also expected different, and sometimes even controversial, features from learning analytics. Therefore, giving students control over the choice of functionalities in learning analytics would be reasonable to consider in order to develop customizable and individually meaningful learning analytics. Also, in order to support self-regulated learning, it should be made sure that learning analytics provides feedback from all phases of self-regulated learning, since students experienced that the visualizations failed to provide support for planning future studies.Opiskelijoiden kokemuksia oppimisanalytiikan käytöstä akateemisessa ohjauksessa itsesäätöisen oppimisen tukemiseksi. Tiivistelmä. Tämä laadullinen pro gradu -tutkielma sijoittui oppimisanalytiikan ja itsesäätöisen oppimisen leikkauspisteeseen, jossa akateeminen ohjaus toimi kontekstina. Itsesäätöisen oppimisen tarkastelu rajautui käyttäytymisen säätelyyn ja tarkemmin kolmeen resurssienhallintastrategiaan: ajanhallintaan, ponnistelujen säätelyyn ja avun hakemiseen. Oppimisanalytiikan teemasta tarkastelu rajautui AnalytiikkaÄly-hankkeessa kehitettyihin omaopettajaohjauksessa käytettäviin visualisointeihin. Vaikka opiskelijoiden mukaan ottaminen oppimisanalytiikan sovellusten kehittämisprosesseihin on tiedostettu olevan tärkeää, tällä hetkellä on olemassa vain muutamia tutkimuksia aiheeseen liittyen. Tämän työn päätavoitteena on paikata tätä aiempien tutkimusten puutetta tarjoamalla syvempää ymmärrystä siitä, miten itsesäätöistä oppimista voidaan tukea oppimisanalytiikan avulla opiskelijoiden itsensä mukaan. Tarkemmin olin kiinnostunut löytämään vastauksia kolmeen tutkimuskysymykseen liittyen opiskelijoiden omiin haasteisiin ja tuentarpeisiin resurssienhallintastrategioista ja opintojen etenemisestä, heidän kokemuksiinsa kehitteillä olevista visualisoinneista sekä opiskelijoiden toiveista ja odotuksista visualisointien jatkokehittämiselle. Tutkittavat koostuivat kymmenestä Oulun yliopiston opiskelijasta, jotka osallistuivat AnalytiikkaÄly-hankkeen pilottitutkimukseen lukuvuonna 2019–2020. Aineistonkeruu tapahtui puolistrukturoitujen haastattelujen kautta hyödyntäen stimulated recall-metodia ja aineisto analysoitiin laadullisella teoriaohjaavalla sisällönanalyysilla, jossa haastattelujen litteroinnit toimivat tutkimusmateriaalina. Tutkimustulokset osoittivat tähän opinnäytetyöhön valikoituneiden tutkittavien olevan hyvin pärjääviä ja yleisesti omaavan vain vähäisiä haasteita ja tuentarpeita. Opiskelijoilla oli myös erilaisia mieltymyksiä kehitteillä olevista visualisoinneista ja niiden käytöstä ohjauksessa, mikä näyttäytyi eriävinä kokemuksina. Yleisesti opiskelijat kokivat kuvaajien onnistuvan visualisoimaan haasteita ja tuen tarpeita, jonka takia ne koettiin hyödyllisiksi erityisesti enemmän haasteita omaaville opiskelijoille. Opiskelijat myös odottivat oppimisanalytiikalta erilaisia ja toisinaan jopa vastakkaisia ominaisuuksia. Tästä syystä kontrollin antaminen opiskelijoille voisi olla mielekästä, jotta voitaisiin kehittää yksilöllisesti muokattavissa olevia ja siten merkitykselliseksi koettavia oppimisanalytiikan sovelluksia. Jotta itsesäätöistä oppimista voidaan tukea tehokkaasti, tulisi myös keskittyä tarjoamaan palautetta kaikkiin sen vaiheisiin liittyen, sillä tällä hetkellä opiskelijoiden kokemusten mukaan tulevien opintojen suunnittelu ei tule tarpeeksi tuetuksi