by J. Biggs and C. Tang, Maidenhead, England,
Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Teaching for quality learning at university

Human activity recognition systems are developed as part of a framework to enable continuous monitoring of human behaviours in the area of ambient assisted living, sports injury detection, elderly care, rehabilitation, and entertainment and surveillance in smart home environments. The extraction of relevant features is the most challenging part of the mobile and wearable sensor-based human activity recognition pipeline. Feature extraction influences the algorithm performance and reduces computation time and complexity. However, current human activity recognition relies on handcrafted features that are incapable of handling complex activities especially with the current influx of multimodal and high dimensional sensor data. With the emergence of deep learning and increased computation powers, deep learning and artificial intelligence methods are being adopted for automatic feature learning in diverse areas like health, image classification, and recently, for feature extraction and classification of simple and complex human activity recognition in mobile and wearable sensors. Furthermore, the fusion of mobile or wearable sensors and deep learning methods for feature learning provide diversity, offers higher generalisation, and tackles challenging issues in human activity recognition. The focus of this review is to provide in-depth summaries of deep learning methods for mobile and wearable sensor-based human activity recognition. The review presents the methods, uniqueness, advantages and their limitations. We not only categorise the studies into generative, discriminative and hybrid methods but also highlight their important advantages. Furthermore, the review presents classification and evaluation procedures and discusses publicly available datasets for mobile sensor human activity recognition. Finally, we outline and explain some challenges to open research problems that require further research and improvements.

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Creative and critical thinking skills are the abilities, which can sometimes be used interchangeably in definition. In fact, they have different constructs because they differentiate in outcome of human behaviours. Also one of today’s requirements is that individuals should approach everyday problems by using both competences. So, one of the helpful tool for development of creativity and critical thinking skills proposed is problem-based learning environments in classrooms. In this study, problem based approach including philosophy; general characteristics of it, role of teachers and students in problem-based learning environment, and its uniqueness over other learning approaches is explained by considering advantages and limitations. Then, problem-based learning is discussed with regard to instructional design perspective in a scientific manner. Finally, the study is aimed to explain when PBL grounded in development of instructional strategy as a fruitful approach; instructional strategies, methods and techniques are differentiated in creativity and critical thinking skills.

/pdf/creative-and-critical-thinking-skills-in-problem-based-2abrg21zwn.pdf

Creative and Critical Thinking Skills in Problem-based Learning Environments

This study investigates the effects of Problem Based Learning (PBL) assisted with Augmented Reality (AR) on learning achievement and attitude towards physics subjects as a part of science education. The sample of the study included 91 seventh graders from a province in the north of Turkey. A quasi-experimental design with two experimental groups and a control group was utilized. Based upon marker-based AR technologies, FenAR software was developed to support with PBL activities in the classroom. The experimental results indicated that integrating AR into PBL activities both increased students' learning achievement and promoted their positive attitudes towards physics subjects. This technology contributed to students' long-term retention of the concepts in the field of physics. In semi-structured interviews, the students emphasized that AR applications were more useful, realistic, and interesting for their learning; helped them to understand and analyse the problem scenarios. Apart from educational advantages, AR applications may lead to physical disorders among some of the students. It has been suggested that AR technology can be a potential and effective tool for activating students' positive emotions in PBL process. Moreover, implications on use of AR for physics education and recommendations for further studies are also discussed in the study.

Integrating augmented reality into problem based learning: The effects on learning achievement and attitude in physics education

The focus of the present paper is on the contribution of the research in the student approaches to learning tradition. Several studies in this field have started from the assumption that students’ approaches to learning develop towards more deep approaches to learning in higher education. This paper reports on a systematic review of longitudinal research on how students’ approaches to learning develop during higher education. A total of 43 studies were included in the review. The results give an unclear picture of the development of approaches to learning and, thus, do not provide clear empirical evidence for the assumption that students develop towards more deep approaches during higher education. Neither methodological nor conceptual aspects of the studies investigated explained the ambiguity of the research results. Both theoretical and empirical implications for further research are discussed.

/pdf/do-students-develop-towards-more-deep-approaches-to-learning-4o8h501v7l.pdf

Do Students Develop Towards More Deep Approaches to Learning During Studies? A Systematic Review on the Development of Students’ Deep and Surface Approaches to Learning in Higher Education

In problem-based learning (PBL), implemented worldwide, students learn by discussing professionally relevant problems enhancing application and integration of knowledge, which is assumed to encourage students towards a deep learning approach in which students are intrinsically interested and try to understand what is being studied. This review investigates: (1) the effects of PBL on students’ deep and surface approaches to learning, (2) whether and why these effects do differ across (a) the context of the learning environment (single vs. curriculum wide implementation), and (b) study quality. Studies were searched dealing with PBL and students’ approaches to learning. Twenty-one studies were included. The results indicate that PBL does enhance deep learning with a small positive average effect size of .11 and a positive effect in eleven of the 21 studies. Four studies show a decrease in deep learning and six studies show no effect. PBL does not seem to have an effect on surface learning as indicated by a very small average effect size (.08) and eleven studies showing no increase in the surface approach. Six studies demonstrate a decrease and four an increase in surface learning. It is concluded that PBL does seem to enhance deep learning and has little effect on surface learning, although more longitudinal research using high quality measurement instruments is needed to support this conclusion with stronger evidence. Differences cannot be explained by the study quality but a curriculum wide implementation of PBL has a more positive impact on the deep approach (effect size .18) compared to an implementation within a single course (effect size of −.05). PBL is assumed to enhance active learning and students’ intrinsic motivation, which enhances deep learning. A high perceived workload and assessment that is perceived as not rewarding deep learning are assumed to enhance surface learning.

/pdf/deep-and-surface-learning-in-problem-based-learning-a-review-310ezcqbki.pdf

Hélène Marcq

Papers

Deep and surface learning in problem-based learning: a review of the literature