Effects of Technology Assisted Flat Learning Environment for a Design Project at a Historically Black University

2017 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique ◽  
Firas Akasheh ◽  
Gül E. Okudan Kremer
Keyword(s):  
Problem Solving ◽  
Correlation Analysis ◽  
Learning Environment ◽  
Phase I ◽  
Student Learning ◽  
Phase Ii ◽  
Information Exchange ◽  
Peer To Peer ◽  
Learning Gains ◽  
Paired T Test

Teaching to Learn (TeatoL) is a flat learning environment where peer-to-peer information exchange has been demonstrated to bridge student learning gaps. Within TeatoL, we integrate and expand peer-to-peer knowledge exchange facilitated by technology, in order to enhance the learning of engineering graduates, using an open-ended authentic life problem in design for manufacturing. One of the main objectives for developing TeatoL was to improve the interest and efficacy of underrepresented minority (URM) students in online engineering courses. In this paper, we present our TeatoL implementation at Tuskegee University (TU), to assess the effectiveness of TeatoL in improving student learning and in enhancing ill-structured problem solving skills of URM students. The participants in the learning environment were given an open design problem related to casting process. A short lecture about 35 minutes (Phase 0) was given and then each student team created and uploaded an instructional material (video) on their approach for solving the open-ended problem using computers and mobile phones (Phase I). The students then critically evaluated and posted feedbacks on these peer videos (Phase II). The final step of the process involved students writing a short report on their modified problem solving process and then applied the process to the same open-ended problem (Phase III). The students used comments from peers and information from other videos to modify and improve their approaches. Student learning in all three phases (Phase I through III) was assessed to understand the effects of different modes of learning in TeatoL. Paired t-test, regression and correlation analysis were used to determine the learning gains and how learning happens in a flat learning environment. Paired t-test analysis showed that there were significant learning gains from peer information exchange in TeatoL. Regression and Correlation analysis suggests that number of in-depth comments exchanged during Phase II depends on the initial level of knowledge; and learning gains of students depend on the meaningful comments provided by their peers. The results suggest that, in an online environment, peer-to-peer information exchange in the form of feedback can be particularly useful to attract, retain and train URM students as well as academically underprepared students.

Bridging Learning Gap Through Peer-to-Peer Information Exchange in a Flat Environment

2015 ◽  
Author(s):  
Madhumitha Ramachandran ◽  
Zahed Siddique ◽  
Gül E. Okudan Kremer ◽  
Firas Akasheh
Keyword(s):  
Problem Solving ◽  
Learning Environment ◽  
Learning Community ◽  
Information Exchange ◽  
Peer Interactions ◽  
Peer To Peer ◽  
Phase Iii ◽  
Instructional Video ◽  
Learning Gap ◽  
Develop Technology

In this paper, we present a technology assisted flat learning environment, Teaching to Learn (TeatoL), where all participants have dual roles as students and instructors. The main objective of this work is to investigate how peer-to-peer information exchange aids in bridging knowledge gap in a flat-learning environment. We present our TeatoL implementation that was developed to enhance ill-structured problem solving skill along with its assessment. The participants in the learning environment were given an open design problem related to sheet metal forming. A short lecture about 35 minutes (Phase 0) was given and then student teams were asked to make an instructional video (Phase I) describing their approach for solving the open-ended problem. The videos were viewed by peers, using their computers and mobile devices. The students then critiqued and provided feedback on the posted videos (Phase II). The final step of the process had students write short reports on their problem solving approach (Phase III) that was modified based on peer-to-peer interactions. Student learning in all three phases was assessed to understand the effects of different modes of learning in TeatoL. Our findings indicate that TeatoL is an effective flat online learning environment. Correlation analysis suggests that learning gains are dependent on the level of knowledge on the topic for the learning community (class) and the number of meaningful comments provided by peers. The findings from this work can be utilized to develop technology based online peer learning environments to improve learning outcomes through active collaborative learning. Such an environment can be particularly useful for open course delivery.

Modeling students’ problem solving performance in the computer-based mathematics learning environment

2017 ◽  
Vol 34 (5) ◽  
pp. 385-395 ◽  
Author(s):  
Young-Jin Lee
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Learning Environment ◽  
Student Learning ◽  
Mathematics Learning ◽  
Quantitative Model ◽  
Content Type ◽  
Computer Based Learning ◽  
Instructional Scaffolding ◽  
Computer Based

Purpose The purpose of this paper is to develop a quantitative model of problem solving performance of students in the computer-based mathematics learning environment. Design/methodology/approach Regularized logistic regression was used to create a quantitative model of problem solving performance of students that predicts whether students can solve a mathematics problem correctly based on how well they solved other problems in the past. The usefulness of the model was evaluated by comparing the predicted probability of correct problem solving to the actual problem solving performance on the data set that was not used in the model building process. Findings The regularized logistic regression model showed a better predictive power than the standard Bayesian Knowledge Tracing model, the most frequently used quantitative model of student learning in the Educational Data Mining research. Originality/value Providing instructional scaffolding is critical in order to facilitate student learning. However, most computer-based learning environments use heuristics or rely on the discretion of students when they determine whether instructional scaffolding needs be provided. The predictive model of problem solving performance of students can be used as a quantitative guideline that can help make a better decision on when to provide instructional supports and guidance in the computer-based learning environment, which can potentially maximize the learning outcome of students.

Bewegungstherapie und körperliche Aktivität bei Patienten mit Herzinsuffizienz

Praxis ◽  
2018 ◽  
Vol 107 (17-18) ◽  
pp. 951-958 ◽  
Author(s):  
Matthias Wilhelm
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
Körperliche Aktivität ◽  
Phase Iii

Zusammenfassung. Herzinsuffizienz ist ein klinisches Syndrom mit unterschiedlichen Ätiologien und Phänotypen. Die überwachte Bewegungstherapie und individuelle körperliche Aktivität ist bei allen Formen eine Klasse-IA-Empfehlung in aktuellen Leitlinien. Eine Bewegungstherapie kann unmittelbar nach Stabilisierung einer akuten Herzinsuffizienz im Spital begonnen werden (Phase I). Sie kann nach Entlassung in einem stationären oder ambulanten Präventions- und Rehabilitationsprogramm fortgesetzt werden (Phase II). Typische Elemente sind Ausdauer-, Kraft- und Atemtraining. Die Kosten werden von der Krankenversicherung für drei bis sechs Monate übernommen. In erfahrenen Zentren können auch Patienten mit implantierten Defibrillatoren oder linksventrikulären Unterstützungssystemen trainieren. Wichtiges Ziel der Phase II ist neben muskulärer Rekonditionierung auch die Steigerung der Gesundheitskompetenz, um die Langzeit-Adhärenz bezüglich körperlicher Aktivität zu verbessern. In Phase III bieten Herzgruppen Unterstützung.

How to Make COVID-19 Contact Tracing Apps work: Insights from Behavioral Economics (Preprint)

2021 ◽  
Author(s):  
Ian Ayres ◽  
Alessandro Romano ◽  
Chiara Sotis
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
Network Effects ◽  
Risky Behaviors ◽  
Contact Tracing ◽  
Risk Compensation ◽  
Main Function ◽  
Double Blind ◽  
Compensation Theory ◽  
Pros And Cons

BACKGROUND Due to network effects, Contact Tracing Apps (CTAs) are only effective if many people download them. However, the response to CTAs has been tepid. For example, in France less than 2 million people (roughly 3% of the population) downloaded the CTA. Consequently, CTAs need to be fundamentally rethought to increase their effectiveness. OBJECTIVE This study aimed to show that CTAs can still play a key role in containing the pandemic, provided that they take into account insights from behavioral sciences. Moreover, we study whether emphasizing the virtues of CTA to induce people to download them makes app users engage in more risky behaviors (risk compensation theory) and whether feedback on a user’s behavior affects future behaviors. METHODS We perform a double-blind online experiment (n=1500) divided in two phases. In Phase I respondents are randomly assigned to one of three different groups: Pros of the app, Pros and Cons of the app and Control I. Respondents in the Pros group were shown information on the advantages of CTAs. Participants in the Pros and Cons group were shown information on both the advantages and the problems that characterize CTAs. Last, respondents in the Control I group were not given any information on CTAs. All participants are then asked how worried they are about the pandemic, how likely they are to download the app, and on how they intend to behave (e.g. attend small and large gathering, wear a mask, etc.). A week later we carried out Phase II. Participants in Phase II were randomly assigned to different in-app notifications in which they were informed on how much risk they were taking compared to the average user. We then ask participants their intentions for future behaviors to investigate whether these notifications were effective in making respondents more prudent. RESULTS All 1500 participants completed phase I of the experiment, whereas 1303 (86.9%) completed also phase 2. The main findings are: i) informing people on the pros of the app make them less worried about the pandemic (p=.004), ii) informing people about both the pros and the cons of the app makes them more likely to download the app (p=.07); iii) carefully devised in-app notification induce people to state that they will: attend less large gatherings (p= .05) and less small gatherings (p= .001), see less people at risk (p=.004), that they stay more at home (p=.006) and wear more often the mask (p=.09). We do not find support for the risk-compensation theory. CONCLUSIONS we suggest that CTAs should be re-framed as Behavioral Feedback Apps (BFAs). The main function of BFAs would be providing users with information on how to minimize the risk of contracting COVID-19, e.g. to provide information on how crowded a store is likely to be at a given time of the day. Moreover, the BFA could have a rating system that allows users to flag stores that do not respect safety norms, such as mandating customers to wear a mask or not respecting social distancing. These functions can inform the behavior of app users, thus playing a key role in containing the spread of the virus even if a small percentage of people download the BFA. While effective contact tracing is impossible when only 3% of the population downloads the app, less risk taking by small portions of the population can produce large benefits. BFAs can be programmed so that users can also activate a tracing function akin to the one currently carried out by CTAs. Making contact tracing an ancillary, opt-in function might facilitate a wider acceptance of BFAs.

scholarly journals Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out using ATR-FTIR spectroscopy and chemometric approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tanurup Das ◽  
Abhimanyu Harshey ◽  
Ankit Srivastava ◽  
Kriti Nigam ◽  
Vijay Kumar Yadav ◽  
...  
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
Ex Vivo ◽  
Body Fluids ◽  
Biochemical Changes ◽  
Partial Least Square ◽  
The Body ◽  
Slow Phase ◽  
Time Since Deposition ◽  
Dry Out

AbstractThe ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm−1 and 1600–1000 cm−1 are the major contributors to the spectra of these fluids. Several peaks in the spectral region between 1600 and 1000 cm−1 showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.

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