Do Flipped Learning and Adaptive Instruction Improve Student Learning Outcome? A Case Study of a Computer Programming Course in Taiwan

Flipped learning could improve the learning effectiveness of students. However, some studies have pointed out the limitations related to flipped classrooms because the content of the flipped course does not vary according to the needs of the students. On the other hand, adaptive teaching, which customizes the learning mode according to the individual needs of students, can make up for some of the shortcomings of flipped teaching. This study combines adaptive teaching with flipped teaching and applies it to face-to-face classroom activities. The purpose of this research is to explore whether the implementation of flipping and adaptive learning in a computer programming course can improve the learning effectiveness of students. The experimental subjects of this study are the sophomore students in the Department of Information Management. The flipped classroom with adaptive instruction has been realized in the limited course time. This study uses questionnaires to collect pre- and post-test data on the “learning motivation” of students. The learning effectiveness was evaluated based on the students' previous programming course (C language) and the semester scores of this course. Research results show that the post-test “learning motivation” has improved overall compared with the pre-test, and the learning effect is significant. The results of this research not only prove the effectiveness of modern teaching theories in programming courses but also lay the foundation for future teaching design.

No child left behind, nor singled out: is it possible to combine adaptive instruction and inclusive pedagogy in early math software?

The article addresses the challenge of combining adaptive and inclusive instruction in early math software, that is, to provide different kinds of support and challenges to different individuals in response to their different needs—yet avoid exposing children (whether far behind or far ahead) as being different. Arguments for adaption as well as inclusion are discussed, and an evaluative user study is conducted in which 42 3- to 6-year-old preschool children made use of a digital play-&-learn game for early math designed to combine adaptive instruction with inclusion during a period of 6 weeks. Data logging, performance measures, observations of children playing, and interviews with teachers are used to evaluate whether the adaptive and inclusive strategies worked out as intended. Results indicate that the goals of inclusion as well as the goals of adaptivity were met. A preliminary conclusion is that it is possible to combine adaptation and inclusion in early math software.

Do Student Ratings Provide Reliable and Valid Information about Teaching Quality at the School Level? Evaluating Measures of Science Teaching in PISA 2015

Large-scale educational surveys, including PISA, often collect student ratings to assess teaching quality. Because of the sampling design in PISA, student ratings must be aggregated at the school level instead of the classroom level. To what extent does school-level aggregation of student ratings yield reliable and valid measures of teaching quality? We investigate this question for six scales measuring classroom management, emotional support, inquiry-based instruction, teacher-directed instruction, adaptive instruction, and feedback provided by PISA 2015. The sample consisted of 503,146 students from 17,678 schools in 69 countries/regions. Multilevel CFA and SEM were conducted for each scale in each country/region to evaluate school-level reliability (Intraclass Correlations 1 and 2), factorial validity, and predictive validity. In most countries/regions, school-level reliability was found to be adequate for the Classroom Management scale, but only low to moderate for the other scales. Examination of factorial and predictive validity indicated that the Classroom Management, Emotional Support, Adaptive Instruction, and Teacher-directed Instruction scales capture meaningful differences in teaching quality between schools. Meanwhile, the Inquiry scale exhibited poor validity in almost all countries/regions. These findings suggest the possibility of using student ratings in PISA to investigate some aspects of school-level teaching quality in most countries/regions.

Co-designing Gamified Intelligent Tutoring Systems with Teachers

Researchers are increasingly interested in Gamified Intelligent Tutoring Systems (ITSs) to provide adaptive instruction and to enhance engagement of students. However, although teachers are demanding to be active users of gamified ITS, they have been not considered as first-class citizens in the design of these kinds of systems. In order to contribute to the active and customized use of gamified ITS by teachers, three technical problems should be considered. First, designing ITS is very complex (i.e., considering different theories, components, and stakeholders) and including gamification may significantly increase such complexity and variability. Second, gamified ITS features can be used depending on several elements (e.g., educational level, knowledge domain, gamification and ITS theories, etc). Thus, it is imperative to take advantage of theories and practices from both topics to reduce the design space of these systems. Third, in order to effectively aid teachers to actively use such systems, it is needed to provide a simple and usable solution for them. To target these problems, in this paper, we present a solution for authoring gamified ITS by teachers that makes use of an ontology-based feature model (OntoSPL) to deal with the variability at runtime and takes advantage of an ontology (GaTO) that connects gamified ITS theories and design practices to constrain the variability space for designing these systems. Our main results indicate that teachers have a high acceptance level (i.e., ease of use, usability, and low complexity) in the design of gamified ITS using the authoring solution, customizing their own gamified tutors in less than five minutes. These results indicate a promising way to explore the use of authoring tools, ontologies, and software engineering to take advantage of both artificial intelligence techniques (mainly for aiding adaptation for students) as well as on the human intelligence of teachers to co-design gamified intelligent tutoring systems.