26 papers · ranked by Valyu relevance
Heather A. Johnson, Laura Barrett
Objective The purpose of this study was to compare two pedagogical methods, active learning and passive instruction, to determine which is more useful in helping students to achieve the learning outcomes in a one-hour research skills instructional session. Methods Two groups of high school students attended an…
Amol Kulkarni, Janis Terpenny, Vittaldas Prabhu, Tao Peng
Identifying failure modes is an important task to improve the design and reliability of a product and can also serve as a key input in sensor selection for predictive maintenance. Failure mode acquisition typically relies on experts or simulations which require significant computing resources. With the recent advances…
Marcos E García-Ojeda, Michele K Nishiguchi
Teaching students at all levels of education has undergone extensive changes, particularly in the past decade. Our present student population has transformed dramatically in the 21st century due to the changing demographics of the nation, an increasing use of technology both inside and outside the classroom, along with…
Xinjian Cen, Rachel J. Lee, Christopher Contreras, Melinda T. Owens + 1 more
Active learning, including student thinking and discussion in class, has been shown to increase student learning gains. However, it is less clear how variations in how instructors implement active learning affect student gains. Our study aims to investigate the extent to which the time spent on individual episodes of…
Judit Sánchez, Marta Lesmes, Clara Azpeleta, Beatriz Gal
Background Engaging, student-centered active learning activities, such as team-based learning (TBL) and laboratory practices, is beneficial to integrate knowledge, particularly in Medicine degree. Previously, we designed and implemented workstation learning activities (WSLA) inspired by TBL, which proved effective for…
Mallory A. Jackson, Hongjiao Liu, Sungmin Moon, Jennifer H. Doherty + 1 more
Myriad studies support the claim that active learning improves student academic performance in STEM, yet lecture remains the dominant form of instruction. Faculty offer multiple reasons for not using active learning with many expressing confusion as to what active learning is. Contributing to that confusion is the fact…
Mariel A. Pfeifer, Julio J. Cordero, Julie Dangremond Stanton
STEM instructors are encouraged to adopt active learning in their courses, yet our understanding of how active learning affects different groups of students is still developing. One group often overlooked in higher education research is students with disabilities. Two of the most commonly occurring disabilities on…
Simon Viet Johansson, Hampus Gummesson Svensson, Esben Bjerrum, Alexander Schliep + 3 more
Computer aided synthesis planning is a rapidly growing field for suggesting synthetic routes for molecules of interest. The methods used are usually dependent on access to large datasets for training, but with a finite experimental budget there are limitations on how much data can be obtained from experiments. Active…
Jaeseo Lim, Hwiyeol Jo, Byoung‐Tak Zhang, Jooyong Park
Although the use of active learning to increase learners' engagement has recently been introduced in a variety of methods, empirical experiments are lacking. In this study, we attempted to align two experiments in order to (1) make a hypothesis for machine and (2) empirically confirm the effect of active learning on…
Anna Jo J. Auerbach, Tessa C. Andrews
Background Though active-learning instruction has the potential to positively impact the preparation and diversity of STEM graduates, not all instructors are able to achieve this potential. One important factor is the teacher knowledge that instructors possess, including their pedagogical knowledge. Pedagogical…
Izadora Volpato Rossi, Jordana Dinorá de Lima, Bruna Sabatke, Maria Alice Ferreira Nunes + 2 more
Active teaching methodologies have been placed as a hope for changing education at different levels, transiting from passive lecture-centered to student-centered learning. With the health measures of social distance, the COVID-19 pandemic forced a strong shift to remote education. With the challenge of delivering…
Yannick Ureel, Maarten R. Dobbelaere, Yi Ouyang, Kevin De Ras + 3 more
By combining machine learning with design of experiments, so-called active machine learning, more efficient and cheaper research can be conducted. Machine learning algorithms are more flexible, and are better at investigating the processes spanning all length scales of chemical engineering. While the active machine…
Hideitsu Hino
In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling.…
Raquel Lopes, Ana Baptista, Sergii Tukaiev, Vaitsa Giannouli + 1 more
The integration of digital tools in teaching remains a challenge for many educators, often due to resistance to change and limited training. Drawing on Self-Determination Theory, as proposed by Deci and Ryan, and Cognitive Load Theory, developed by Sweller, this exploratory study examines student motivation and…
Chaoqi Wang, Adish Singla, Yuxin Chen
We study the problem of active learning with the added twist that the learner is assisted by a helpful teacher. We consider the following natural interaction protocol: At each round, the learner proposes a query asking for the label of an instance x q , the teacher provides the requested label {x q , yq} along with…
Paul Francoeur, Daniel Penaherrera, David Koes
The immense size of chemical space, the relative scarcity of high quality data, and the cost of running experiments to accurately measure molecular properties makes active learning (AL) an attractive approach to efficiently explore the space and train high-quality models for molecular property prediction. While AL is…
Derek van Tilborg, Francesca Grisoni
Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active deep learning has an untapped potential for low-data drug discovery, as it allows to improve a model iteratively during the screening…
Ji-Ung Lee, Christian M. Meyer, Iryna Gurevych
Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling…
Laura Desirèe Di Paolo, Ben White, Avel Guénin-Carlut, Axel Constant + 1 more
'Andy Clark'] Human learning essentially involves embodied interactions with the material world. But our worlds now include increasing numbers of powerful and (apparently) disembodied generative artificial intelligence (AI). In what follows we ask how best to understand these new (somewhat ‘alien’, because of their…
Lorelei Patrick, Leigh Anne Howell, E. William Wischusen
Despite many calls to reform undergraduate science, technology, engineering, and math (STEM) education to incorporate active learning into classes, there has been little attention paid to graduate level classrooms or courses taught by graduate students. Here, we set out to understand if and how STEM graduate students’…
Julian Roelle, Claudia Müller, Detlev Roelle, Kirsten Berthold + 1 more
'Michael A Motes'] Although instructional explanations are commonly provided when learners are introduced to new content, they often fail because they are not integrated into effective learning activities. The recently introduced active-constructive-interactive framework posits an effectiveness hierarchy in which…
Melanie M Cooper, Marcos D. Caballero, Justin H. Carmel, Erin M. Duffy + 11 more
In recent years, much of the emphasis for transformation of introductory STEM courses has focused on “active learning”, and while this approach has been shown to produce more equitable outcomes for students, the construct of “active learning” is somewhat ill-defined, and can encompass a wide range of pedagogical…
Weiyang Liu, Bo Dai, Xingguo Li, James M. Rehg + 1 more
In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to…
Authors not listed
Developing generalizable machine learning models with minimal data remains a central challenge in materials informatics. Effective models can significantly reduce costly computational simulations and time-intensive experimentation by providing reliable predictions of material properties. In this work, we investigate…
Sid Ijju
Active learning algorithms have been an integral part of recent advances in artificial intelligence. However, the research in the field is widely varying and lacks an overall organizing leans. We outline a Markovian formalism for the field of active learning and survey the literature to demonstrate the organizing…
Yuriy Khalak, Gary Tresadern, David F. Hahn, Bert L. de Groot + 1 more
Drug discovery can be thought as a search for a needle in a haystack. Finding the initial active hit molecules, the optimal decoration of lead molecule analogues, to final clinical candidate selection is an on-going trade-off between applying the best methods versus the cost of assessing the large available chemical…