相关论文: Active Learning for Out-of-class Activities by Usi…

A wealth of Open Educational Resources is now available, and beyond the first and evident problem of finding them, the issue of articulating a set of resources is arising. When using audiovisual resources, among different possibilities,…

The main aim of this paper is to discuss how the combination of Web 2.0, social media and geographic technologies can provide opportunities for learning and new forms of participation in an urban design studio. This discussion is mainly…

Data modeling and graphing skill sets are foundational to science learning and careers, yet students regularly struggle to master these basic competencies. Further, although educational researchers have uncovered numerous approaches to…

In many industrial applications, obtaining labeled observations is not straightforward as it often requires the intervention of human experts or the use of expensive testing equipment. In these circumstances, active learning can be highly…

The global aging trend compels older adults to navigate the evolving digital landscape, presenting a substantial challenge in mastering smartphone applications. While Augmented Reality (AR) holds promise for enhancing learning and user…

Collaborative learning environments such as programming labs are crucial for learning experiential hands-on skills such as critical thinking and problem solving, and peer discussion. In a traditional laboratory setting, many of these skills…

Active learning strategies respond to the costly labelling task in a supervised classification by selecting the most useful unlabelled examples in training a predictive model. Many conventional active learning algorithms focus on refining…

Active Learning is a very common yet powerful framework for iteratively and adaptively sampling subsets of the unlabeled sets with a human in the loop with the goal of achieving labeling efficiency. Most real world datasets have imbalance…

Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the…

Multi-agent AI systems, which simulate diverse instructional roles such as teachers and peers, offer new possibilities for personalized and interactive learning. Yet, student-AI interaction patterns and their pedagogical implications remain…

Of late, mobile technology has introduced new, novel environment that can be capitalized to further enrich the teaching and learning process in classrooms. Taking cognizance of this promising setting, a study was undertaken to investigate…

The birth of massive open online courses (MOOCs) has had an undeniable effect on how teaching is being delivered. It seems that traditional in class teaching is becoming less popular with the young generation, the generation that wants to…

College students routinely use online course catalogs to explore a variety of academic offerings. Course catalogs may therefore be an effective place to encourage reflection on academic choices and interests. To test this, we embedded a…

Learning agents that excel at sequential decision-making tasks must continuously resolve the problem of exploration and exploitation for optimal learning. However, such interactions with the environment online might be prohibitively…

As the application of AI continues to expand, students in technology programs are poised to be both producers and users of the technologies. They are also positioned to engage with AI applications within and outside the classroom. While…

This paper presents different possibilities of using mobile robots in education. Through the application of mobile mechatronic robotic system "Robotino" this paper shows the possibilities of developing interactive lectures and exercises in…

A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions. This paper presents the results of automatically intervening on struggling…

Labeling each instance in a large dataset is extremely labor- and time- consuming . One way to alleviate this problem is active learning, which aims to which discover the most valuable instances for labeling to construct a powerful…

Active learning (AL) aims to enable training high performance classifiers with low annotation cost by predicting which subset of unlabelled instances would be most beneficial to label. The importance of AL has motivated extensive research,…

Teens are using mobile devices for an increasing number of activities. Smartphones and a variety of mobile apps for communication, entertainment, and productivity have become an integral part of their lives. This mobile phone use has…