Learn Smarter, Not Harder – Exploring the Development of Learning Analytics Use Cases to Create Tailor-Made Online Learning Experiences

Our world is significantly shaped by digitalization, fostering new opportunities for technology-mediated learning. Therefore, massive amounts of knowledge become available online. However, concurrently these formats entail less interaction and guidance from lecturers. Thus, learners need to be supported by intelligent learning tools that provide suitable knowledge in a tailored way. In this context, the use of learning analytics in its multifaceted forms is essential. Existing literature shows a proliferation of learning analytics use cases without a systematic structure. Based on a structured literature review of 42 papers we organized existing literature contributions systematically and derived four use cases: learning dashboards, individualized content, tutoring systems, and adaptable learning process based on personality. Our use cases will serve as a basis for a targeted scientific discourse and are valuable orientation for the development of future learning analytics use cases to give rise to the new form of Learning Experience Platforms

Identifying Player Types to Tailor Game-Based Learning Design to Learners:Cross-sectional Survey using Q Methodology

BACKGROUND: Game-based learning appears to be a promising instructional method because of its engaging properties and positive effects on motivation and learning. There are numerous options to design game-based learning; however, there is little data-informed knowledge to guide the choice of the most effective game-based learning design for a given educational context. The effectiveness of game-based learning appears to be dependent on the degree to which players like the game. Hence, individual differences in game preferences should be taken into account when selecting a specific game-based learning design. OBJECTIVE: We aimed to identify patterns in students' perceptions of play and games-player types and their most important characteristics. METHODS: We used Q methodology to identify patterns in opinions on game preferences. We recruited undergraduate medical and dental students to participate in our study and asked participants to sort and rank 49 statements on game preferences. These statements were derived from a prior focus group study and literature on game preferences. We used by-person factor analysis and varimax rotation to identify common viewpoints. Both factors and participants' comments were used to interpret and describe patterns in game preferences. RESULTS: From participants' (n=102) responses, we identified 5 distinct patterns in game preferences: the social achiever, the explorer, the socializer, the competitor, and the troll. These patterns revolved around 2 salient themes: sociability and achievement. The 5 patterns differed regarding cheating, playing alone, story-telling, and the complexity of winning. CONCLUSIONS: The patterns were clearly interpretable, distinct, and showed that medical and dental students ranged widely in how they perceive play. Such patterns may suggest that it is important to take students' game preferences into account when designing game-based learning and demonstrate that not every game-based learning-strategy fits all students. To the best of our knowledge, this study is the first to use a scientifically sound approach to identify player types. This can help future researchers and educators select effective game-based learning game elements purposefully and in a student-centered way

Learning Models in Educational Game Interactions: A Review

Educational games have now been used as innovative media and teaching strategies to achieve more effective learning and have an impact that tends to be very good in the learning process. However, it is important to know and systematically prove that the application of the learning model in the interaction of educational games is indeed feasible to be adopted and has an effect. This paper aims to present empirical evidence of the current situation regarding the application of learning models in the flow of educational game interactions. The method used is a systematic literature review by adopting three main stages, namely: 1) Planning; 2) Implementation; 3) Reporting. Then recommend the ten steps in the systematic literature review process along with the selection process through the test-retest approach. The initial search obtained 1,405,310 papers, then go through the selection stage. The selection process took place at stage B1 with the number of papers that successfully passed 198, at the B2 selection stage there were 102 papers, and we focus 75 papers that have passed a fairly rigorous screening and selection process on the quality assessment process for primary studies, used to answer research objectives and questions. We can confirm and conclude that 75 papers have applied the learning model in educational game interactions. The dominating domain is Education, the type of game that dominates is Educational Game, for the most dominating subjects are Programming, Student Learning Motivation as the most dominating impact, Experimental Design as a trial technique, the most widely used evaluation instruments are Questionnaires and Tests, a population that dominates between 79-2,645 people, and 8 papers to support learning in vocational education

Why so serious?:game-based learning in health profession education: state of the art and future directions

If you look around carefully, you see a lot of use of game elements that aim to motivate people towards a certain behaviour. From smileys on posts that aim to lower your driving speed, to earning stars in language learning apps. Game-based learning is the use of game elements to make learning more attractive and to encourage people to continue their learning. This is logical right? The longer you learn, the better the outcome. Or not? This doctoral thesis examines the effects of using game-based learning in medical education. Why and when should it be applied? We have investigated whether it is advisable to develop a game suitable for everyone. We discovered that there are 5 different game personas (player types): competitors, socializers, social achievers, explorers and trolls. Everyone has their own preferences when it comes to social interactions and achieving goals within a game. From this we were able to develop a taxonomy, which has been tested at almost all medical universities in the Netherlands. It shows that medical students are mainly socially oriented players. While most game based learnings are not at all. This doctoral research can offer perspective in current developments, gives direction where it could go, but also has a critical note on the use of game-based learning that is should not be applied too much

Um modelo de perfil de aluno voltado a aplicações de técnicas de learning analytics

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2019.A análise das interações dos alunos com os ambientes virtuais de aprendizagem assumiu um papel relevante para decisões educacionais. A grande disponibilidade de cursos a distância permite o uso da tecnologia a fim de explorar os dados produzidos a partir dessas alterações. Pode assim, maximizar o aprendizado dos alunos, sugerindo atividades de acordo com o perfil de cada um. Entretanto, a utilização do perfil do aluno para análises mais abrangentes ainda é insipiente. Neste sentido, o presente trabalho propõe um modelo de dados de perfil de aluno voltado a aplicação de técnicas de Learning Analytics em Sistemas de Aprendizagem Online. O modelo, elaborado por meio do desenvolvimento de artefatos, teve como suporte a metodologia Design Science Research. Para a sua avaliação, utilizou-se uma base de dados de uma instituição de ensino que possui atividades ativas em um ambiente virtual de aprendizagem. A partir desses dados, foi possível a aplicação das técnicas escolhidas, obtendo-se informações relevantes para subsidiar os gestores no âmbito educacional. Análises estatísticas, análise de agrupamentos e sistemas de recomendação foram as técnicas aplicadas. De maneira geral, os resultados produzidos estão centrados na identificação e geração de grupos de perfis similares, considerando o estilo de aprendizagem e o tipo de personalidade dos alunos. Esta estratégia permitiu a obtenção de resultados promissores para a tomada de decisão no contexto educacional e com potencial para gerar uma contribuição efetiva para a área de Learning Analytics.Abstract: The analysis of students' interactions with virtual learning environments has assumed a relevant role for educational decisions. The wide availability of distance learning courses allows the use of technology to exploit the data produced from these interactions. It can thus maximize students' learning by suggesting activities according to their profile. However, using the student profile for broader analysis is still incipient. In this sense, the present work proposes a student profile data model, focused on the application of Learning Analytics techniques in Online Learning Systems. The model, created through the development of artifacts, was supported by the Design Science Research methodology. For its evaluation, it was used a database from an educational institution that has active activities in a virtual learning environment. From these data, it was possible to apply the chosen techniques, obtaining relevant information to support managers in the educational field. Statistical analyzes, cluster analysis and recommendation systems were the applied techniques. In general, the results produced focus on the identification and generation of similar profile groups, considering the students' learning style and personality type. This strategy allowed promising results for decision making in the educational context and with the potential to generate an effective contribution to the area of Learning Analytics