17 research outputs found
Dual Attention Networks for Visual Reference Resolution in Visual Dialog
Visual dialog (VisDial) is a task which requires an AI agent to answer a
series of questions grounded in an image. Unlike in visual question answering
(VQA), the series of questions should be able to capture a temporal context
from a dialog history and exploit visually-grounded information. A problem
called visual reference resolution involves these challenges, requiring the
agent to resolve ambiguous references in a given question and find the
references in a given image. In this paper, we propose Dual Attention Networks
(DAN) for visual reference resolution. DAN consists of two kinds of attention
networks, REFER and FIND. Specifically, REFER module learns latent
relationships between a given question and a dialog history by employing a
self-attention mechanism. FIND module takes image features and reference-aware
representations (i.e., the output of REFER module) as input, and performs
visual grounding via bottom-up attention mechanism. We qualitatively and
quantitatively evaluate our model on VisDial v1.0 and v0.9 datasets, showing
that DAN outperforms the previous state-of-the-art model by a significant
margin.Comment: EMNLP 201
Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models
The present study aims to investigate similarities between how humans and
connectionist models experience difficulty in arithmetic problems. Problem
difficulty was operationalized by the number of carries involved in solving a
given problem. Problem difficulty was measured in humans by response time, and
in models by computational steps. The present study found that both humans and
connectionist models experience difficulty similarly when solving binary
addition and subtraction. Specifically, both agents found difficulty to be
strictly increasing with respect to the number of carries. Another notable
similarity is that problem difficulty increases more steeply in subtraction
than in addition, for both humans and connectionist models. Further
investigation on two model hyperparameters --- confidence threshold and hidden
dimension --- shows higher confidence thresholds cause the model to take more
computational steps to arrive at the correct answer. Likewise, larger hidden
dimensions cause the model to take more computational steps to correctly answer
arithmetic problems; however, this effect by hidden dimensions is negligible.Comment: 7 pages; 15 figures; 5 tables; Published in the proceedings of the
17th International Conference on Cognitive Modelling (ICCM 2019
Learning to Write with Coherence From Negative Examples
Coherence is one of the critical factors that determine the quality of
writing. We propose writing relevance (WR) training method for neural
encoder-decoder natural language generation (NLG) models which improves
coherence of the continuation by leveraging negative examples. WR loss
regresses the vector representation of the context and generated sentence
toward positive continuation by contrasting it with the negatives. We compare
our approach with Unlikelihood (UL) training in a text continuation task on
commonsense natural language inference (NLI) corpora to show which method
better models the coherence by avoiding unlikely continuations. The preference
of our approach in human evaluation shows the efficacy of our method in
improving coherence.Comment: 4+1 pages, 4 figures, 2 tables. ICASSP 2022 rejecte
Effect of active learning and online discussions on the academic performances of dental students
COVID-19 caused significant confusion around the world, and dental education was no exception. Therefore, in line with the demands of the times, this study sought to determine the applicability of online active learning to dental education.
This study was conducted in the second semester of 2020 at a school of dentistry in a selective university in Korea. A total of 114 dental students were recruited. Participants were assigned to four different groups (lecture and discussion [LD], lecture and discussion with instructors worksheet [LW], self-study and discussion [SSD], and self-study and discussion with instructors worksheet [SW]) using the random breakout room function in the Zoom video conference application. Their final test scores were then analyzed using analysis of variance and the online active learning results were compared with the offline learning results.
The scores were highest for the transfer type items in the SSD group, followed by the SW group and the two lecture groups, which had no significant differences. These scores and pattern differences between the groups were similar for all items. The results suggested that studying by oneself rather than simply listening to lectures enhanced the effects of the discussions and led to higher learning outcomes. In addition, the effect of the instructor's intervention in the middle of the discussion varied depending on the pre-learning activities of discussion.As with previous offline experiments, self-study followed by group discussion had higher learning outcomes for both the verbatim and transfer type items.
In agreement with the Interactive, Constructive, Active, and Passive (ICAP)framework and other active learning theories, the findings clearly indicated that online active learning was applicable to dental students, and when self-study precedes discussion, the learning is richer and the learning outcomes are better
The impact of writing on academic performance for medical students
Abstract
Background
Writing is a useful learning activity that promotes higher-order thinking, but there are limited studies that prove its effectiveness. In previous research, researchers tested the effect of summary writing on students comprehension and found no significant difference from that of re-studying texts. The purpose of this study, therefore, is to expand previous findings and investigate the effect of two types of writing tasks on medical students academic performance, specifically in the transfer of knowledge.
Methods
An experiment was conducted with 139 medical students from Seoul National University College of Medicine. They were randomly assigned to three study conditions: self-study (SS), expository writing (EW), and argumentative writing (AW) group. Each group studied the given material by the method they were assigned, and they were tested on their comprehension and transfer of knowledge using rote-memory type items and transfer type items respectively.
Results
The results showed that the two writing groups displayed better performance than the SS group in transfer type items, while there was no difference in scores between the EW and AW group. However, the three groups showed no significant difference in their scores for rote-memory type items. Also, there was a positive correlation between the writing scores and transfer type item scores in the AW group.
Conclusions
This study provides empirical evidence for writing to be adopted in medical education for greater educational benefits. Our findings indicate that writing can enhance learning and higher-order thinking, which are critical for medical students
Active learning through discussion: ICAP framework for education in health professions
Background
The ICAP framework based on cognitive science posits four modes of cognitive engagement: Interactive, Constructive, Active, and Passive. Focusing on the wide applicability of discussion as interactive engagement in medical education, we investigated the effect of discussion when it was preceded by self-study and further investigated the effect of generating questions before discussions.
Methods
This study was conducted in the second semester of 2018 and was participated in by 129 students majoring in health professions, including medicine, dentistry, veterinary medicine, and nursing. The students were assigned to four different trial groups and were asked to fill out a Subjective Mental Effort Questionnaire after completing each session. Their performance in posttest scores was analyzed using Bonferroni test, and mental effort was analyzed using mediation analysis.
Results
These results indicated that the self-study and question group had the highest performance and that the lecture and summary group had the lowest performance when comparing the total score. Using the analysis of mental effort, it was confirmed that the relationship between different study conditions and post-test performance was mediated by mental effort during test.
Conclusions
Our findings support the ICAP framework and provide practical implications for medical education, representing the fact that students learn more when they are involved in active learning activities, such as self-study and question generation, prior to discussions.This work was supported by Research Resettlement Fund, funded by Seoul National University to allow the new faculty to design and develop new teaching and learning paradigms in higher education
Recommended from our members
Effect of Active Pre-Learning Activities on Humans and Machines
There are numerous studies that show that the more students actively participate in class, the more they learn. Despiteample evidence, education still relies on lecturers or professors. Although active learning to increase learnersโ engagementhas recently been introduced in a variety of methods, quantitative and empirical experiments are lacking. In this study,we conducted two experiments in order to empirically confirm the effect of active learning on learning performance. Wecompared humans and machines to investigate that active learning is more effective than passive learning. In Experiment1, we compared watching a lecture, the passive form of learning with having a discussion, the active form of learning.Comparing studentsโ learning performance of each condition, results of the present study showed higher performancein active learning. In the additional experiment that imitated the human learning frameworks in machines, the activelearning framework performed better than the passive learning framework. Through the results of humans experiment andvalidation of machines experiment, we found that active learning have crucial effect on learning performance