Machine and expert judgments of student perceptions of teaching behavior in secondary education:Added value of topic modeling with big data

Research shows that effective teaching behavior is important for students' learning and outcomes, and scholars have developed various instruments for measuring effective teaching behavior domains. Although student assessments are frequently used for evaluating teaching behavior, they are mainly in Likert-scale or categorical forms, which precludes students from freely expressing their perceptions of teaching. Drawing on an open-ended questionnaire from large-scale student surveys, this study uses a machine learning tool aiming to extract teaching behavior topics from large-scale students’ open-ended answers and to test the convergent validity of the outcomes by comparing them with theory-driven manual coding outcomes based on expert judgments. We applied a latent Dirichlet allocation (LDA) topic modeling analysis, together with a visualization tool (LDAvis), to qualitative data collected from 173,858 secondary education students in the Netherlands. This data-driven machine learning analysis yielded eight topics of teaching behavior domains: Clear explanation, Student-centered supportive learning climate, Lesson variety, Likable characteristics of the teacher, Evoking interest, Monitoring understanding, Inclusiveness and equity, Lesson objectives and formative assessment. In addition, we subjected 864 randomly selected student responses from the same dataset to manual coding, and performed theory-driven content analysis, which resulted in nine teaching behavior domains and 19 sub-domains. Results suggest that the relation between machine learning and human analysis is complementary. By comparing the bottom-up (machine learning analysis) and top-down (content analysis), we found that the proposed topic modeling approach reveals unique domains of teaching behavior, and confirmed the validity of the topic modeling outcomes evident from the overlapping topics

Adaptive serious games for computer science education

Serious games have the potential to effectively engage students to learn, however, these games tend to struggle accommodating learners with diverse abilities and needs. Furthermore, customizing a serious game to the individual learner has historically required a great deal of effort on the part of subject matter experts, and is not always feasible for increasingly complex games. This thesis proposes the use of automatic methods to adapt serious programming games to learners' abilities. To understand the context of the problem, a survey was conducted of the serious programming game literature, which found that while many games exist, there has been very little consideration for the use of adaptation. Given the breadth of the existing serious programming game literature, a methodology was developed to support adaptation of existing games. To demonstrate the efficacy of this adaptive methodology in serious programming games, two case studies were conducted: 1) a study comparing adaptive and non-adaptive gameplay in the Gidget game, and 2) a study assessing non-adaptive gameplay, adaptive gameplay, and adaptive hints in the RoboBug game. The results from both case studies provide evidence to the need for adaptation in serious programming games, and illustrate how the adaptive methodology can be utilized to positively affect the engagement of learners and their ability to achieve learning outcomes

Challenges 2017: aprender nas nuvens, learning in the clouds: atas da X Conferência Internacional de Tecnologias de Informação e Comunicação na Educação

A conferência Challenges comemora em 2017 a sua décima edição, consolidando, assim, o reconhecimento como um dos mais significativos eventos no âmbito da educação com as novas tecnologias em Portugal. Ao longo destas dez edições a Challenges afirma-se como um marco indelével na agenda dos investigadores, educadores e professores portugueses de todos os níveis de ensino, formando uma comunidade dinâmica que, recorrentemente, traz consigo novos colegas. A cada edição, a Challenges conquista novos “adeptos” e expande-se além-fronteiras, chegando à generalidade dos países lusófonos e a outros, como a Espanha ou a Venezuela, o Reino Unido ou a Mongólia, tornando-se num espaço de debate intercontinental! A X Conferência Internacional de Tecnologias de Informação e Comunicação na Educação – Challenges 2017, recebeu mais de 200 participantes, para além de oradores convidados e membros das diversas comissões, e contou com a apresentação pública de 109 comunicações orais e 22 apresentações em formato poster, cujos textos se publicam neste livro de atas. No contexto de uma sociedade cada vez mais digital, o envolvimento de cerca de 300 autores faz com que a Challenges se afirme como um espaço de partilha e de reflexão no domínio da investigação e da inovação educacional relacionada com as Tecnologias de Informação e Comunicação. O lema “Aprender nas nuvens, Learning in the clouds”, adotado nesta décima edição da Challenges, impõe-se pelas referências tecnológicas implícitas que nos remetem para a computação e para a aprendizagem em rede e na rede e para a mobilidade, mas, “Aprender nas nuvens, Learning in the clouds”, pelo seu plural, remete-nos também para leituras adicionais, para outros significados.“Aprender nas nuvens, Learning in the clouds”, por similitude com “andar nas nuvens”, remete-nos para a esfera do sonho e da fantasia, da paixão e do entusiasmo. Com “Aprender nas nuvens, Learning in the clouds” é a esse entusiasmo que quisemos prestar homenagem. O entusiasmo de aprender numa sociedade em constante mudança, num tempo em que o potencial das tecnologias nos leva para mundos muito diversos.info:eu-repo/semantics/publishedVersio