How Does Prior Knowledge Impact Students’ Online Learning Behaviors?

2013 ◽  
pp. 97-115
Author(s):  
Kirsten R. Butcher ◽  
Tamara Sumner
Keyword(s):  
Online Learning ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Learning Task ◽  
Factual Knowledge ◽  
Student Interactions ◽  
Digital Resources ◽  
Web Based ◽  
Learning Behaviors ◽  
Frequent Use

This study explored the impact of prior domain knowledge on students’ strategies and use of digital resources during a Web-based learning task. Domain knowledge was measured using pre- and posttests of factual knowledge and knowledge application. Students utilized an age- and topic-relevant collection of 796 Web resources drawn from an existing educational digital library to revise essays that they had written prior to the online learning task. Following essay revision, participants self-reported their strategies for improving their essays. Screen-capture software was used to record all student interactions with Web-based resources and all modifications to their essays. Analyses examined the relationship between different levels of students’ prior knowledge and online learning behaviors, self-reported strategies, and learning outcomes. Findings demonstrated that higher levels of factual prior knowledge were associated with deeper learning and stronger use of digital resources, but that higher levels of deep prior knowledge were associated with less frequent use of online content and fewer deep revisions. These results suggest that factual knowledge can serve as a useful knowledge base during self-directed, online learning tasks, but deeper prior knowledge may lead novice learners to adopt suboptimal processes and behaviors.

How Does Prior Knowledge Impact Students’ Online Learning Behaviors?

2011 ◽  
Vol 1 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Kirsten R. Butcher ◽  
Tamara Sumner
Keyword(s):  
Online Learning ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Learning Task ◽  
Factual Knowledge ◽  
Student Interactions ◽  
Digital Resources ◽  
Web Based ◽  
Learning Behaviors ◽  
The Impact

This study explored the impact of prior domain knowledge on students’ strategies and use of digital resources during a Web-based learning task. Domain knowledge was measured using pre- and posttests of factual knowledge and knowledge application. Students utilized an age- and topic-relevant collection of 796 Web resources drawn from an existing educational digital library to revise essays that they had written prior to the online learning task. Following essay revision, participants self-reported their strategies for improving their essays. Screen-capture software was used to record all student interactions with Web-based resources and all modifications to their essays. Analyses examined the relationship between different levels of students’ prior knowledge and online learning behaviors, self-reported strategies, and learning outcomes. Findings demonstrated that higher levels of factual prior knowledge were associated with deeper learning and stronger use of digital resources, but that higher levels of deep prior knowledge were associated with less frequent use of online content and fewer deep revisions. These results suggest that factual knowledge can serve as a useful knowledge base during self-directed, online learning tasks, but deeper prior knowledge may lead novice learners to adopt suboptimal processes and behaviors.

scholarly journals Finding the optimal Bayesian network given a constraint graph

2017 ◽  
Vol 3 ◽  
pp. e122 ◽  
Author(s):  
Jacob M. Schreiber ◽  
William S. Noble
Keyword(s):  
Bayesian Network ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Structure Learning ◽  
Learning Task ◽  
Computational Time ◽  
Constraint Graph ◽  
Intuitive Method ◽  
Time Required ◽  
Optimal Graph

Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a “constraint graph” as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.

scholarly journals Finding the optimal bayesian network given a constraint graph

2017 ◽  
Author(s):  
Jacob M Schreiber ◽  
William S Noble
Keyword(s):  
Bayesian Network ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Structure Learning ◽  
Learning Task ◽  
Computational Time ◽  
Constraint Graph ◽  
Intuitive Method ◽  
Time Required ◽  
Optimal Graph

Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a "constraint graph" as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.

scholarly journals Finding the optimal bayesian network given a constraint graph

2017 ◽  
Author(s):  
Jacob M Schreiber ◽  
William S Noble
Keyword(s):  
Bayesian Network ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Structure Learning ◽  
Learning Task ◽  
Computational Time ◽  
Constraint Graph ◽  
Intuitive Method ◽  
Time Required ◽  
Optimal Graph

Despite recent algorithmic improvements, learning the optimal structure of a Bayesian network from data is typically infeasible past a few dozen variables. Fortunately, domain knowledge can frequently be exploited to achieve dramatic computational savings, and in many cases domain knowledge can even make structure learning tractable. Several methods have previously been described for representing this type of structural prior knowledge, including global orderings, super-structures, and constraint rules. While super-structures and constraint rules are flexible in terms of what prior knowledge they can encode, they achieve savings in memory and computational time simply by avoiding considering invalid graphs. We introduce the concept of a "constraint graph" as an intuitive method for incorporating rich prior knowledge into the structure learning task. We describe how this graph can be used to reduce the memory cost and computational time required to find the optimal graph subject to the encoded constraints, beyond merely eliminating invalid graphs. In particular, we show that a constraint graph can break the structure learning task into independent subproblems even in the presence of cyclic prior knowledge. These subproblems are well suited to being solved in parallel on a single machine or distributed across many machines without excessive communication cost.

Baseball fans don’t like lumpy batters: Influence of domain knowledge on the access of subordinate meanings

2018 ◽  
Vol 71 (1) ◽  
pp. 93-102 ◽  
Author(s):  
Jennifer Wiley ◽  
Tim George ◽  
Keith Rayner
Keyword(s):  
Prior Knowledge ◽  
Lexical Access ◽  
Domain Knowledge ◽  
Negative Evidence ◽  
Word Meanings ◽  
Positive Evidence ◽  
Ambiguous Words ◽  
Baseball Fans ◽  
Evidence Condition ◽  
Access Model

Two experiments investigated the effects of domain knowledge on the resolution of ambiguous words with dominant meanings related to baseball. When placed in a sentence context that strongly biased toward the non-baseball meaning (positive evidence), or excluded the baseball meaning (negative evidence), baseball experts had more difficulty than non-experts resolving the ambiguity. Sentence contexts containing positive evidence supported earlier resolution than did the negative evidence condition for both experts and non-experts. These experiments extend prior findings, and can be seen as support for the reordered access model of lexical access, where both prior knowledge and discourse context influence the availability of word meanings.

On-device Prior Knowledge Incorporated Learning for Personalized Atrial Fibrillation Detection

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Zhenge Jia ◽  
Yiyu Shi ◽  
Samir Saba ◽  
Jingtong Hu
Keyword(s):  
Atrial Fibrillation ◽  
Prior Knowledge ◽  
Domain Knowledge ◽  
Fine Tuning ◽  
Cardiac Monitoring ◽  
Patient Specific ◽  
Detection Accuracy ◽  
Specific Patient ◽  
Deep Model ◽  
Tuning Strategy

Atrial Fibrillation (AF), one of the most prevalent arrhythmias, is an irregular heart-rate rhythm causing serious health problems such as stroke and heart failure. Deep learning based methods have been exploited to provide an end-to-end AF detection by automatically extracting features from Electrocardiogram (ECG) signal and achieve state-of-the-art results. However, the pre-trained models cannot adapt to each patient’s rhythm due to the high variability of rhythm characteristics among different patients. Furthermore, the deep models are prone to overfitting when fine-tuned on the limited ECG of the specific patient for personalization. In this work, we propose a prior knowledge incorporated learning method to effectively personalize the model for patient-specific AF detection and alleviate the overfitting problems. To be more specific, a prior-incorporated portion importance mechanism is proposed to enforce the network to learn to focus on the targeted portion of the ECG, following the cardiologists’ domain knowledge in recognizing AF. A prior-incorporated regularization mechanism is further devised to alleviate model overfitting during personalization by regularizing the fine-tuning process with feature priors on typical AF rhythms of the general population. The proposed personalization method embeds the well-defined prior knowledge in diagnosing AF rhythm into the personalization procedure, which improves the personalized deep model and eliminates the workload of manually adjusting parameters in conventional AF detection method. The prior knowledge incorporated personalization is feasibly and semi-automatically conducted on the edge, device of the cardiac monitoring system. We report an average AF detection accuracy of 95.3% of three deep models over patients, surpassing the pre-trained model by a large margin of 11.5% and the fine-tuning strategy by 8.6%.

Live-action mass-casualty training and virtual world training

2016 ◽  
Vol 60 (1) ◽  
pp. 2103-2107 ◽  
Author(s):  
Keith T. Shubeck ◽  
Scotty D. Craig ◽  
Xiangen Hu
Keyword(s):  
Virtual World ◽  
Domain Knowledge ◽  
Mass Casualty ◽  
Learning Technologies ◽  
Learning Needs ◽  
Web Based ◽  
Tutoring Systems ◽  
Aeromedical Evacuation ◽  
User Input ◽  
Live Action

Live-action training simulations with expert facilitators are considered by many to be the gold-standard in training environments. However, these training environments are expensive, provide many logistical challenges, and may not address the individual’s learning needs. Fortunately, advances in distance-based learning technologies have provided the foundation for inexpensive and effective learning environments that can simultaneously train and educate students on a much broader scale than live-action training environments. Specifically, intelligent tutoring systems (ITSs) have been proven to be very effective in improving learning outcomes. The Virtual Civilian Aeromedical Evacuation Sustainment Training (VCAEST) interface takes advantage of both of these technologies by enhancing a virtual world with a web-based ITS, AutoTutor LITE (Learning in Interactive Training Environments). AutoTutor LITE acts as a facilitator in the virtual world by providing just-in-time feedback, presenting essential domain knowledge, and by utilizing tutoring dialogues that automatically assess user input. This paper will discuss the results of an experimental evaluation of the VCAEST environment compared to an expert-led live-action training simulation.

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