scholarly journals Recommender in AI-enhanced Learning: An Assessment from the Perspective of Instructional Design

2020 ◽  
Vol 2 (1) ◽  
pp. 101-111
Author(s):  
Michael Kerres ◽  
Katja Buntins
Keyword(s):  
Instructional Design ◽  
Recommender Systems ◽  
Learning Experience ◽  
Social Comparisons ◽  
Future Research ◽  
Learning Paths ◽  
Context Of Learning ◽  
Individual Profiles ◽  
Enhanced Learning

AbstractAs tools for AI-enhanced human learning, recommender systems support learners in finding materials and sequencing learning paths. The paper explores how these recommenders improve the learning experience from a perspective of instructional design. It analyzes mechanisms underlying current recommender systems, and it derives concrete examples of how they operate: Recommenders are either expert-, criteria-, behavior-, or profile-based or rely on social comparisons. To verify this classification of five different mechanisms, we analyze a set of current publications on recommenders and find all the identified mechanisms with profile-based approaches as the most common. Social recommenders, though highly attractive in other sectors, reveal some drawbacks in the context of learning. In comparison, expert-based recommendations are easy to implement and often stand out as simple but effective ways for suggesting learning materials and learning paths to learners. They can be combined with other approaches based on social comparisons and individual profiles. The paper points out challenges in studying recommenders for learning and provides suggestions for future research.

scholarly journals Learning Design: European Approaches

TechTrends ◽  
2020 ◽  
Vol 64 (6) ◽  
pp. 815-827
Author(s):  
Barbara Wasson ◽  
Paul A. Kirschner
Keyword(s):  
Instructional Design ◽  
Learning Experience ◽  
New Technology ◽  
The United States ◽  
Instructional Materials ◽  
Future Research ◽  
Learning Design ◽  
Mind Set ◽  
Learning And Cognition ◽  
Instructional Design Principles

Abstract Research on instructional and learning design is ‘booming’ in Europe, although there has been a move from a focus on content and the way to present it in a formal educational context (i.e., instruction), to a focus on complex learning, learning environments including the workplace, and access to learner data available in these environments. We even see the term ‘learning experience design’ (Neelen and Kirschner 2020) to describe the field. Furthermore, there is an effort to empower teachers (and even students) as designers of learning (including environments and new pedagogies), and to support their reflection on their own practice as part of their professional development (Hansen and Wasson 2016; Luckin et al. 2016; Wasson et al. 2016). While instructional design is an often heard term in the United States and refers to “translating principles of learning and instruction into plans for instructional materials, activities, information resources, and evaluation” (Smith and Ragan 1999), Europe tends to lean more towards learning design as the key for providing efficient, effective, and enjoyable learning experiences. This is not a switch from an instructivist to a constructivist view nor from a teacher-centred to a student-centred paradigm. It is, rather, a different mind-set where the emphasis is on the goal (i.e., learning) rather than the approach (i.e., instruction). Designing learning opportunities in a technology enhanced world builds on theories of human learning and cognition, opportunities provided by technology, and principles of instructional design. New technology both expands and challenges some instructional design principles by opening up new opportunities for distance collaboration, intelligent tutoring and support, seamless and ubiquitous learning and assessment technologies, and tools for thinking and thought. In this article, the authors give an account of their own and other research related to instructional and learning design, highlight related European research, and point to future research directions.

scholarly journals Learning experience design for augmented reality

2021 ◽  
Vol 29 ◽  
Author(s):  
Betül Czerkawski ◽  
Margherita Berti
Keyword(s):  
Augmented Reality ◽  
Mixed Reality ◽  
Learning Experience ◽  
Data Gathering ◽  
Real Life ◽  
Survey Method ◽  
Future Research ◽  
Experience Design ◽  
Learning Tasks ◽  
Enhanced Learning

Recent years have seen a growing interest in augmented reality (AR) technologies due to their potential for simulating real-life situations and creating authentic learning tasks. Studies have shown that AR enables engaging and interactive learning experiences (e.g. Bressler and Bodzin 2013; Klopfer and Sheldon 2010) and can benefit student learning (e.g. Bonner and Reinders 2018; Siegle 2019). However, although research in AR for education is not scarce, educators often do not have a learning experience design (LXD) approach that is supported by the recent findings of learning sciences and instructional design models. To bridge this gap, the present study introduces an AR-learning prototype developed by using SAM I (Successive Approximation Model I), and the Threshold Concepts Framework, employed for meaningful integration of AR into the learning process. A pre-survey and a post-survey method were utilised in the data gathering process to gauge students’ experience with the AR module. The findings show that the majority of students have not had educational experiences with AR prior to the study, and they struggled to find ways to incorporate this technology into their content areas in a meaningful way. Nonetheless, participants realised the value of AR and stated that they most likely would use this technology in the future. Based on the findings, the authors present a set of suggestions for instructors and LXDs, and provide recommendations for future research. This article is part of the special collection: Mobile Mixed Reality Enhanced Learning edited by Thom Cochrane, James Birt, Helen Farley, Vickel Narayan and Fiona Smart. More papers from this collection can be found here.

Versioning Learning to Different Target Learner Groups

2021 ◽  
pp. 157-168
Keyword(s):  
Instructional Design ◽  
Learning Experience ◽  
Target Group ◽  
Learning Groups ◽  
Addition And Subtraction ◽  
Learning Paths ◽  
Definition Of ◽  
Learner Experience

A fairly common practice in instructional design is to originate a new instructional design over new content and then version the learning onto different tracks for different learning groups. Some learners may require a particular learning experience while others do not (based on learner experience mapping). Visual instructional design helps in the segmenting of various learner groups, the definition of various learning paths, various methods for customizing learning through customization, differentiation, addition and subtraction of elements, content revision and editing, cultural overlays, and some whole or partial redesigns for an effective and evocative learning experience for the target group.

Students' Co-Ownership of Summative Assessment Marking Criteria Towards a More Democratic Assessment in Higher Education.

2021 ◽  
pp. 199-223
Author(s):  
Alexander J. Aidan
Keyword(s):  
Higher Education ◽  
Action Research ◽  
Critical Pedagogy ◽  
Learning Experience ◽  
Summative Assessment ◽  
Assessment Process ◽  
Future Research ◽  
Assessment Literacy ◽  
Action Research Project ◽  
Enhanced Learning

This chapter focuses on the second iteration of a longitudinal action research project that culminates in students acting as partners in the assessment process by co-creating their summative assessment marking criteria. The research argues that the co-ownership of marking criteria increases assessment literacy and helps students to understand how their assignment will be marked. The research limitations are presented, and future research pathways are defined. The findings are analyzed from a social-constructivist and critical pedagogy window. The research concludes that actively engaging students in their assessment leads to an enhanced learning experience and creates space for a more democratic assessment in the classroom.

Social Cognitive Theory and Motivation

2019 ◽  
pp. 9-26 ◽  
Author(s):  
Dale H. Schunk ◽  
Ellen L. Usher
Keyword(s):  
Social Cognitive Theory ◽  
Self Efficacy ◽  
Goal Attainment ◽  
Social Cognitive ◽  
Cognitive Theory ◽  
Outcome Expectations ◽  
Social Comparisons ◽  
Future Research ◽  
Regulatory Processes ◽  
Social Environmental

Social cognitive theory is a theory of human behavior that emphasizes learning from the social environment. This chapter focuses on Bandura’s social cognitive theory, which postulates reciprocal interactions among personal, behavioral, and social/environmental factors. Persons use various vicarious, symbolic, and self-regulatory processes as they strive to develop a sense of agency in their lives. Key motivational processes are goals and self-evaluations of progress, outcome expectations, values, social comparisons, and self-efficacy. People set goals and evaluate their goal progress. The perception of progress sustains self-efficacy and motivation. Individuals act in accordance with their values and strive for outcomes they desire. Social comparisons with others provide further information on their learning and goal attainment. Self-efficacy is a critical influence on motivation and affects task choices, effort, persistence, and achievement. Recommendations are made for future research.

A Taxonomy for Psycho-Oncological Intervention Techniques in an Acute Care Hospital in Germany

2021 ◽  
pp. 1-7
Author(s):  
Martina Madl ◽  
Marietta Lieb ◽  
Katharina Schieber ◽  
Tobias Hepp ◽  
Yesim Erim
Keyword(s):  
Acute Care ◽  
Interrater Reliability ◽  
Acute Care Hospital ◽  
Future Research ◽  
Systematic Research ◽  
Care Hospital ◽  
Certification System ◽  
Stepwise Procedure ◽  
Further Development

<b><i>Background:</i></b> Due to the establishment of a nationwide certification system for cancer centers in Germany, the availability of psycho-oncological services for cancer patients has increased substantially. However, little is known about the specific intervention techniques that are applied during sessions in an acute care hospital, since a standardized taxonomy is lacking. With this study, we aimed at the investigation of psycho-oncological intervention techniques and the development of a comprehensive and structured taxonomy thereof. <b><i>Methods:</i></b> In a stepwise procedure, a team of psycho-oncologists generated a data pool of interventions and definitions that were tested in clinical practice during a pilot phase. After an adaptation of intervention techniques, interrater reliability (IRR) was attained by rating 10 previously recorded psycho-oncological sessions. A classification of interventions into superordinate categories was performed, supported by cluster analysis. <b><i>Results:</i></b> Between April and June 2017, 980 psycho-oncological sessions took place. The experts agreed on a total number of 22 intervention techniques. An IRR of 89% for 2 independent psycho-oncological raters was reached. The 22 techniques were classified into 5 superordinate categories. <b><i>Discussion/Conclusion:</i></b> We developed a comprehensive and structured taxonomy of psycho-oncological intervention techniques in an acute care hospital that provides a standardized basis for systematic research and applied care. We expect our work to be continuously subjected to further development: future research should evaluate and expand our taxonomy to other contexts and care settings.

scholarly journals Perfume and Flavor Engineering: A Chemical Engineering Perspective

Molecules ◽  
2021 ◽  
Vol 26 (11) ◽  
pp. 3095
Author(s):  
Alírio E. Rodrigues ◽  
Idelfonso Nogueira ◽  
Rui P.V. Faria
Keyword(s):  
Diffusion Model ◽  
Chemical Engineering ◽  
Reaction Engineering ◽  
Ternary Diagram ◽  
Future Research ◽  
Performance Parameters ◽  
Liquid Equilibrium ◽  
Research Directions ◽  
Future Research Directions

In the last two decades, scientific methodologies for the prediction of the design, performance and classification of fragrance mixtures have been developed at the Laboratory of Separation and Reaction Engineering. This review intends to give an overview of such developments. It all started with the question: what do we smell? The Perfumery Ternary Diagram enables us to determine the dominant odor for each perfume composition. Evaporation and 1D diffusion model is analyzed based on vapor-liquid equilibrium and Fick’s law for diffusion giving access to perfume performance parameters. The effect of matrix and skin is addressed and the trail of perfumes analyzed. Classification of perfumes with the perfumery radar is discussed. The methodology is extended to flavor and taste engineering. Finally, future research directions are suggested.

scholarly journals Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases

Biosensors ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Priya Dave ◽  
Roberto Rojas-Cessa ◽  
Ziqian Dong ◽  
Vatcharapan Umpaichitra
Keyword(s):  
Infectious Diseases ◽  
Dental Health ◽  
The United States ◽  
Human Saliva ◽  
Field Application ◽  
Future Research ◽  
Prevention Measures ◽  
Control And Prevention ◽  
Close Distance

The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission mean of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by sneezing, coughing, breathing, and talking may carry this virus. People in close distance may be exposed directly to these droplets or indirectly when touching the droplets that fall on surrounding surfaces and ending up contracting COVID-19 after touching the mucosa tissue of their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person’s health and diagnose different diseases, ranging from diabetes to dental health, have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the presence of the virus. A classification of sensors by their working principles and the substances they detect is presented, including the sensors’ specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.

Staying the distance: Transforming Latin pedagogy at the Open University

2019 ◽  
Vol 18 (1-2) ◽  
pp. 101-128
Author(s):  
Mair E. Lloyd ◽  
James Robson
Keyword(s):  
Cultural Context ◽  
Learning Experience ◽  
New Technology ◽  
Significant Proportion ◽  
Lessons Learned ◽  
Future Research ◽  
Open University ◽  
Ancient Rome ◽  
Positive Effects ◽  
Pedagogical Changes

Abstract Between 2000 and 2013, over 8,000 students studied the module Reading Classical Latin at the Open University, the United Kingdom’s largest distance education provider. But while many learners attained high grades, a significant proportion withdrew from study or failed the module. In 2015, the original module was replaced with a completely new course, Classical Latin: The Language of Ancient Rome. This article details the innovative ways in which new technology and pedagogical theory from Modern Foreign Language (MFL) learning were drawn on by the team designing this new module, resulting in a learning experience which gives greater emphasis to elements such as spoken Latin, the intrinsic pleasure of reading, and cultural context. The (largely positive) effects of these pedagogical changes on student success and satisfaction are subsequently analysed using a rich mix of qualitative and quantitative data. Finally, the authors reflect on lessons learned and the possibilities for future research and enhancement.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

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