Matching Items (15)

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Does self-regulated learning-skills training improve high-school students' self-regulation, math achievement, and motivation while using an intelligent tutor?

Description

This study empirically evaluated the effectiveness of the instructional design, learning tools, and role of the teacher in three versions of a semester-long, high-school remedial Algebra I course to determine

This study empirically evaluated the effectiveness of the instructional design, learning tools, and role of the teacher in three versions of a semester-long, high-school remedial Algebra I course to determine what impact self-regulated learning skills and learning pattern training have on students' self-regulation, math achievement, and motivation. The 1st version was a business-as-usual traditional classroom teaching mathematics with direct instruction. The 2rd version of the course provided students with self-paced, individualized Algebra instruction with a web-based, intelligent tutor. The 3rd version of the course coupled self-paced, individualized instruction on the web-based, intelligent Algebra tutor coupled with a series of e-learning modules on self-regulated learning knowledge and skills that were distributed throughout the semester. A quasi-experimental, mixed methods evaluation design was used by assigning pre-registered, high-school remedial Algebra I class periods made up of an approximately equal number of students to one of the three study conditions or course versions: (a) the control course design, (b) web-based, intelligent tutor only course design, and (c) web-based, intelligent tutor + SRL e-learning modules course design. While no statistically significant differences on SRL skills, math achievement or motivation were found between the three conditions, effect-size estimates provide suggestive evidence that using the SRL e-learning modules based on ARCS motivation model (Keller, 2010) and Let Me Learn learning pattern instruction (Dawkins, Kottkamp, & Johnston, 2010) may help students regulate their learning and improve their study skills while using a web-based, intelligent Algebra tutor as evidenced by positive impacts on math achievement, motivation, and self-regulated learning skills. The study also explored predictive analyses using multiple regression and found that predictive models based on independent variables aligned to student demographics, learning mastery skills, and ARCS motivational factors are helpful in defining how to further refine course design and design learning evaluations that measure achievement, motivation, and self-regulated learning in web-based learning environments, including intelligent tutoring systems.

Contributors

Agent

Created

Date Created
  • 2013

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Exploring the use of augmented reality to support cognitive modeling in art education

Description

The present study explored the use of augmented reality (AR) technology to support cognitive modeling in an art-based learning environment. The AR application used in this study made visible the

The present study explored the use of augmented reality (AR) technology to support cognitive modeling in an art-based learning environment. The AR application used in this study made visible the thought processes and observational techniques of art experts for the learning benefit of novices through digital annotations, overlays, and side-by-side comparisons that when viewed on mobile device appear directly on works of art.

Using a 2 x 3 factorial design, this study compared learner outcomes and motivation across technologies (audio-only, video, AR) and groupings (individuals, dyads) with 182 undergraduate and graduate students who were self-identified art novices. Learner outcomes were measured by post-activity spoken responses to a painting reproduction with the pre-activity response as a moderating variable. Motivation was measured by the sum score of a reduced version of the Instructional Materials Motivational Survey (IMMS), accounting for attention, relevance, confidence, and satisfaction, with total time spent in learning activity as the moderating variable. Information on participant demographics, technology usage, and art experience was also collected.

Participants were randomly assigned to one of six conditions that differed by technology and grouping before completing a learning activity where they viewed four high-resolution, printed-to-scale painting reproductions in a gallery-like setting while listening to audio-recorded conversations of two experts discussing the actual paintings. All participants listened to expert conversations but the video and AR conditions received visual supports via mobile device.

Though no main effects were found for technology or groupings, findings did include statistically significant higher learner outcomes in the elements of design subscale (characteristics most represented by the visual supports of the AR application) than the audio-only conditions. When participants saw digital representations of line, shape, and color directly on the paintings, they were more likely to identify those same features in the post-activity painting. Seeing what the experts see, in a situated environment, resulted in evidence that participants began to view paintings in a manner similar to the experts. This is evidence of the value of the temporal and spatial contiguity afforded by AR in cognitive modeling learning environments.

Contributors

Agent

Created

Date Created
  • 2016

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Exploring the use of self-explanation prompts in a collaborative learning environment

Description

A recorded tutorial dialogue can produce positive learning gains, when observed and used to promote discussion between a pair of learners; however, this same effect does not typically occur when

A recorded tutorial dialogue can produce positive learning gains, when observed and used to promote discussion between a pair of learners; however, this same effect does not typically occur when an leaner observes a tutorial dialogue by himself or herself. One potential approach to enhancing learning in the latter situation is by incorporating self-explanation prompts, a proven technique for encouraging students to engage in active learning and attend to the material in a meaningful way. This study examined whether learning from observing recorded tutorial dialogues could be made more effective by adding self-explanation prompts in computer-based learning environment. The research questions in this two-experiment study were (a) Do self-explanation prompts help support student learning while watching a recorded dialogue? and (b) Does collaboratively observing (in dyads) a tutorial dialogue with self-explanation prompts help support student learning while watching a recorded dialogue? In Experiment 1, 66 participants were randomly assigned as individuals to a physics lesson (a) with self-explanation prompts (Condition 1) or (b) without self-explanation prompts (Condition 2). In Experiment 2, 20 participants were randomly assigned in 10 pairs to the same physics lesson (a) with self-explanation prompts (Condition 1) or (b) without self-explanation prompts (Condition 2). Pretests and posttests were administered, as well as other surveys that measured motivation and system usability. Although supplemental analyses showed some significant differences among individual scale items or factors, neither primary results for Experiment 1 or Experiment 2 were significant for changes in posttest scores from pretest scores for learning, motivation, or system usability assessments.

Contributors

Agent

Created

Date Created
  • 2018

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Recommender System using Reinforcement Learning

Description

Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users.

Currently, recommender systems are used extensively to find the right audience with the "right" content over various platforms. Recommendations generated by these systems aim to offer relevant items to users. Different approaches have been suggested to solve this problem mainly by using the rating history of the user or by identifying the preferences of similar users. Most of the existing recommendation systems are formulated in an identical fashion, where a model is trained to capture the underlying preferences of users over different kinds of items. Once it is deployed, the model suggests personalized recommendations precisely, and it is assumed that the preferences of users are perfectly reflected by the historical data. However, such user data might be limited in practice, and the characteristics of users may constantly evolve during their intensive interaction between recommendation systems.

Moreover, most of these recommender systems suffer from the cold-start problems where insufficient data for new users or products results in reduced overall recommendation output. In the current study, we have built a recommender system to recommend movies to users. Biclustering algorithm is used to cluster the users and movies simultaneously at the beginning to generate explainable recommendations, and these biclusters are used to form a gridworld where Q-Learning is used to learn the policy to traverse through the grid. The reward function uses the Jaccard Index, which is a measure of common users between two biclusters. Demographic details of new users are used to generate recommendations that solve the cold-start problem too.

Lastly, the implemented algorithm is examined with a real-world dataset against the widely used recommendation algorithm and the performance for the cold-start cases.

Contributors

Agent

Created

Date Created
  • 2020

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The Usefulness of Multi-Sensor Affect Detection on User Experience: An Application of Biometric Measurement Systems on Online Purchasing

Description

Traditional usability methods in Human-Computer Interaction (HCI) have been extensively used to understand the usability of products. Measurements of user experience (UX) in traditional HCI studies mostly rely on task

Traditional usability methods in Human-Computer Interaction (HCI) have been extensively used to understand the usability of products. Measurements of user experience (UX) in traditional HCI studies mostly rely on task performance and observable user interactions with the product or services, such as usability tests, contextual inquiry, and subjective self-report data, including questionnaires, interviews, and usability tests. However, these studies fail to directly reflect a user’s psychological involvement and further fail to explain the cognitive processing and the related emotional arousal. Thus, capturing how users think and feel when they are using a product remains a vital challenge of user experience evaluation studies. Conversely, recent research has revealed that sensor-based affect detection technologies, such as eye tracking, electroencephalography (EEG), galvanic skin response (GSR), and facial expression analysis, effectively capture affective states and physiological responses. These methods are efficient indicators of cognitive involvement and emotional arousal and constitute effective strategies for a comprehensive measurement of UX. The literature review shows that the impacts of sensor-based affect detection systems to the UX can be categorized in two groups: (1) confirmatory to validate the results obtained from the traditional usability methods in UX evaluations; and (2) complementary to enhance the findings or provide more precise and valid evidence. Both provided comprehensive findings to uncover the issues related to mental and physiological pathways to enhance the design of product and services. Therefore, this dissertation claims that it can be efficient to integrate sensor-based affect detection technologies to solve the current gaps or weaknesses of traditional usability methods. The dissertation revealed that the multi-sensor-based UX evaluation approach through biometrics tools and software corroborated user experience identified by traditional UX methods during an online purchasing task. The use these systems enhanced the findings and provided more precise and valid evidence to predict the consumer purchasing preferences. Thus, their impact was “complementary” on overall UX evaluation. The dissertation also provided information of the unique contributions of each tool and recommended some ways user experience researchers can combine both sensor-based and traditional UX approaches to explain consumer purchasing preferences.

Contributors

Agent

Created

Date Created
  • 2018

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Exploring the Efficacy of Using Augmented Reality to Alleviate Common Misconceptions about Natural Selection

Description

Evidence suggests that Augmented Reality (AR) may be a powerful tool for

alleviating certain, lightly held scientific misconceptions. However, many

misconceptions surrounding the theory of evolution are deeply held

Evidence suggests that Augmented Reality (AR) may be a powerful tool for

alleviating certain, lightly held scientific misconceptions. However, many

misconceptions surrounding the theory of evolution are deeply held and resistant to

change. This study examines whether AR can serve as an effective tool for alleviating

these misconceptions by comparing the change in the number of misconceptions

expressed by users of a tablet-based version of a well-established classroom simulation to

the change in the number of misconceptions expressed by users of AR versions of the

simulation.

The use of realistic representations of objects is common for many AR

developers. However, this contradicts well-tested practices of multimedia design that

argue against the addition of unnecessary elements. This study also compared the use of

representational visualizations in AR, in this case, models of ladybug beetles, to symbolic

representations, in this case, colored circles.

To address both research questions, a one-factor, between-subjects experiment

was conducted with 189 participants randomly assigned to one of three conditions: non

AR, symbolic AR, and representational AR. Measures of change in the number and types

of misconceptions expressed, motivation, and time on task were examined using a pair of

planned orthogonal contrasts designed to test the study’s two research questions.

Participants in the AR-based condition showed a significantly smaller change in

the number of total misconceptions expressed after the treatment as well as in the number

of misconceptions related to intentionality; none of the other misconceptions examined

showed a significant difference. No significant differences were found in the total

number of misconceptions expressed between participants in the representative and

symbolic AR-based conditions, or on motivation. Contrary to the expectation that the

simulation would alleviate misconceptions, the average change in the number of

misconceptions expressed by participants increased. This is theorized to be due to the

juxtaposition of virtual and real-world entities resulting in a reduction in assumed

intentionality.

Contributors

Agent

Created

Date Created
  • 2019

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Experimental evaluation of DEFUSE: online de-escalation training for law enforcement intervening in mental health crises

Description

Training for law enforcement on effective ways of intervening in mental health crises is limited. What is available tends to be costly for implementation, labor-intensive, and requires officers to opt-in.

Training for law enforcement on effective ways of intervening in mental health crises is limited. What is available tends to be costly for implementation, labor-intensive, and requires officers to opt-in. DEFUSE, an interactive online training program, was specifically developed to train law enforcement on mental illness and de-escalation skills. Derived from a stress inoculation framework, the curriculum provides education, skills training, and rehearsal; it is brief, cost-effective, and scalable to officers across the country. Participants were randomly assigned to either the experimental or delayed treatment control conditions. A multivariate analysis of variance yielded a significant treatment-by-repeated-measures interaction and univariate analyses confirmed improvement on all of the measures (e.g., empathy, stigma, self-efficacy, behavioral outcomes, knowledge). Replication dependent t-test analyses conducted on the control condition following completion of DEFUSE confirmed significant improvement on four of the measures and marginal significance on the fifth. Participant responses to BPAD video vignettes revealed significant differences in objective behavioral proficiency for those participants who completed the online course. DEFUSE is a powerful tool for training law enforcement on mental illness and effective strategies for intervening in mental health crises. Considerations for future study are discussed.

Contributors

Agent

Created

Date Created
  • 2017

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Exploring the impact of varying levels of augmented reality to teach probability and sampling with a mobile device

Description

Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out

Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from a pre- and posttest given before and after interacting with one of three types of augmented reality experiences, a high AR experience (interacting with three dimensional images coupled with movement through a physical space), a low AR experience (interacting with three dimensional images without movement), or no AR experience (two dimensional images without movement). Two levels of collaboration (pairs and no pairs) were also included. Additionally, student perceptions toward collaboration opportunities and engagement were compared across the six treatment conditions. Other demographic information collected included the students' previous statistics experience, as well as their comfort level in using mobile devices. The moderating variables included prior knowledge (high, average, and low) as measured by the student's pretest score. Taking into account prior knowledge, students with low prior knowledge assigned to either high or low AR experience had statistically significant higher learning gains than those assigned to a no AR experience. On the other hand, the results showed no statistical significance between students assigned to work individually versus in pairs. Students assigned to both high and low AR experience perceived a statistically significant higher level of engagement than their no AR counterparts. Students with low prior knowledge benefited the most from the high AR condition in learning gains. Overall, the AR application did well for providing a hands-on experience working with statistical data. Further research on AR and its relationship to spatial cognition, situated learning, high order skill development, performance support, and other classroom applications for learning is still needed.

Contributors

Agent

Created

Date Created
  • 2013

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Improving adolescent writing quality and motivation with Sparkfolio, a social media based writing tool

Description

Writing instruction poses both cognitive and affective challenges, particularly for adolescents. American teens not only fall short of national writing standards, but also tend to lack motivation for school writing,

Writing instruction poses both cognitive and affective challenges, particularly for adolescents. American teens not only fall short of national writing standards, but also tend to lack motivation for school writing, claiming it is too challenging and that they have nothing interesting to write about. Yet, teens enthusiastically immerse themselves in informal writing via text messaging, email, and social media, regularly sharing their thoughts and experiences with a real audience. While these activities are, in fact, writing, research indicates that teens instead view them as simply "communication" or "being social." Accordingly, the aim of this work was to infuse formal classroom writing with naturally engaging elements of informal social media writing to positively impact writing quality and the motivation to write, resulting in the development and implementation of Sparkfolio, an online prewriting tool that: a) addresses affective challenges by allowing students to choose personally relevant topics using their own social media data; and b) provides cognitive support with a planner that helps develop and organize ideas in preparation for writing a first draft. This tool was evaluated in a study involving 46 eleventh-grade English students writing three personal narratives each, and including three experimental conditions: a) using self-authored social media post data while planning with Sparkfolio; b) using only data from posts authored by one's friends while planning with Sparkfolio; and c) a control group that did not use Sparkfolio. The dependent variables were the change in writing motivation and the change in writing quality that occurred before and after the intervention. A scaled pre/posttest measured writing motivation, and the first and third narratives were used as writing quality pre/posttests. A usability scale, logged Sparkfolio data, and qualitative measures were also analyzed. Results indicated that participants who used Sparkfolio had statistically significantly higher gains in writing quality than the control group, validating Sparkfolio as effective. Additionally, while nonsignificant, results suggested that planning with self-authored data provided more writing quality and motivational benefits than data authored by others. This work provides initial empirical evidence that leveraging students' own social media data (securely) holds potential in fostering meaningful personalized learning.

Contributors

Agent

Created

Date Created
  • 2014

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Improving Usability and Adoption of Tablet-based Electronic Health Record (EHR) Applications

Description

The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records

The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation.

Contributors

Agent

Created

Date Created
  • 2018