Experiences with faculty (McCormick, Gonyea & Kinzie, 2013)
The sample strategy was a convenience sample, since participants were volunteers [18] . It consisted of 39 fourth-semester medical students from the Immunology course which gave consent for the results to be used for educational research purposes.
This distance education model started implementation in early April 2020. In order to achieve an active class, the implementation required prior planning work in which the topics were agreed to be discussed in each session, clear rules of etiquette were established for interaction in the virtual course, as well as materials to be completed before class.
Regarding technical preparations, the teacher logged into the Zoom video conferencing tool on two different devices: computer and tablet. The purpose of the computer session is for the teacher to periodically review the Zoom chat with questions or comments that the students may have, to have an extra screen to corroborate the transmission of the class and to manage the waiting room of the Zoom session. The tablet was used to share the screen where the diagrams were being worked using the Goodnotes app. As the session progressed, the teacher used questions to guide the students' discussion. Together they built the graphic representation that included drawings or annotations. Sometimes screenshots of figures from a book, paper or videos were overlapped into the diagram in Goodnotes to complement the explanation. Altogether, this ensured the class remained interactive, favoring student's engagement and this digital whiteboard was the keystone on achieving it.
In this app, the key concepts and arrows that demonstrate the interaction between the various immunological or hematological processes are integrated. Fig. 1 presents an example of these sessions, it particularly depicts a sequence of events on the platelet activation process. First a table was made (upper left corner) comparing the main glycoproteins on the platelet's surface and their ligands. The bottom right corner showcases a diagram showing step by step platelet activation and involvement of these glycoproteins from adhesion to agregation.
Platelet activation
The construction of the diagram starts from the top left corner, continues towards the right side, and finishes at the bottom of the board. Colors complement the presentation of information in an organized way, helping students to achieve knowledge organisation. Students can take screenshots as the class progresses, but they can also access the diagrams through an online shared-folder where each class is documented.
The items that received the most favorable responses were: “10. I think my teacher showed great commitment making the transition to the distance education model”, “4. The use of graphic resources (whiteboard, drawings, mental maps, integration of text figures) helped me to understand abstract concepts that I find difficult to understand in books”, and “1. I enjoyed the methodology in which my class was taught in the face-to-face format”, with mean of 4.94, 4.83 and 4.8, and variances of 0.053, 0.31 and 0.33 respectively.
The items that received a less favorable evaluation correspond to the items of "7. Switching from the face-to-face diagram construction to a digital version of the whiteboard made it difficult for me to follow the course content.", "8. I felt more involved with the course in the distance course", and "10. I was more motivated to participate in the course in person”, with a mean of 2.3, 3 and 3.63, and variances of 2.22, 1.77 and 1.55 respectively. Table 2 presents the results obtained by each engagement factor.
Student engagement assessment in the implemented innovation
Interaction with peers and faculty | Learning with peers and | 1. I enjoyed the dynamics and interaction of developing diagrams in which my class was taught. | 4.81 | 0.33 |
Experiences with faculty | 2. The format and dynamics of the favored interaction with the teacher. | 3.75 | 1.11 | |
Structure and educational environment | Campus environment | 3. I think my teacher showed great commitment making the transition of class to this distance model | 4.94 | 0.05 |
Structure-dependent engagement | 4. The inclusion of multiple resources and stimuli in the classes, kept my interest. | 4.72 | 0.38 | |
Structure-dependent engagement | 5. I would recommend my friends participating in courses that use a similar format. | 4.56 | 0.83 | |
Adaptive cognition | 6. The digital whiteboard helped me to understand abstract concepts. | 4.83 | 0.31 | |
Impeding/maladaptive cognition | 7. Switching from the face-to-face diagram construction to a digital version of the whiteboard made it difficult for me to follow the course. * | 2.31 | 2.22 | |
Emotion and behavior | Emotional | 8. I felt more involved with the course in the distance course. | 3.00 | 1.77 |
Behavioral Disaffection | 9. The educational experience I received in the face-to-face format was better than the one I have remotely.* | 3.64 | 1.32 | |
Behavioral Disaffection | 10. I was more motivated to participate in the course in person. | 3.64 | 1.55 |
*Reverse scored items
In the open-ended questions, students identified that in face-to-face settings the most relevant engagement factors were 51.2% interaction with peers and faculty, 41.4% structure and educational environment factors, and 7.4% referred to emotion and behavior factors. In distance education settings , students described that the most relevant engagement factors were 0% interaction with peers and faculty, 78.1% structure and educational environment factors, and 21.9% declared emotion and behavior factors. Exemplary quotes of students reflections in open-ended questions are presented in Table 3 .
Students reflections in open-ended questions
Interaction with peers and faculty | “Communicating with other classmates inspires me to ask questions during class”. (participant 5) “The interpersonal experience, even just seeing other people makes me more aware in a session”. (participant 9) “Interacting with the teacher allowed us to solve doubts as they emerged”. (participant 18) | (no mentions were given to this factor) |
Structure and educational environment | “I enjoyed that the class was very visual and it was easy to follow”. (participant 21) “In the course there was bibliography, I knew that if I read it I at least knew the minimum. After that, it was up to me to find out more. Also the teacher guided the session with what we had read, using the diagrams allowed me to integrate the concepts”. (participant 26) | “Classes were recorded and I was able to watch them over again”. (participant 15) “The teacher adapted to an online format very fast, and she seemed interested to make the explanation of the content crystal clear”. (participant 7) “I enjoyed that we translated the diagrams and dynamic explanations that we had in class to keep some kind of normal in the distance setting”. (participant 13) |
Emotion and behavior | “I felt that I was involved in the session, and it made me want to participate”. (participant 27) “It made me relax, and I wanted to be ready for class because it was an unrepeatable moment that I needed to take advantage of”. (participant 31) | “I was studying at my bed”. (participant 11) “I felt I got to know more of the teacher and talk about life”. (participant 21) “I felt that the teacher was doing her best. I really liked that and motivated me to put all my effort in and learn more, not by memorizing but learning”. (participant 22) |
The innovations implemented in the course were focused in fostering interaction with peers and faculty . These adaptations presented a challenge because some of the strategies were not the best to promote interaction and engage students. Tools such as the digital whiteboard were considered useful because they helped to preserve some of the usual conditions and class dynamics. The results of this study show that students felt the class as if nothing had changed from the presence based interactions. That perception of quality was deeply valued since the migration to remote learning was done fast and efficiently given the short-time there was to plan and adapt contents to strategies that were already designed. However, it posed the question to consider if some of the teaching rituals are strictly necessary. Practices such as delivering paper-based assignments, organizing synchronous team-discussions, and long lectures, are to be replaced by the incorporation of some technologies. These additions could contribute to protecting class time for the important elements described above: describing examples of specific processes, discussion with peers, posing questions, and overall constructing knowledge with previous conceptions as learning takes place.
The structure and educational environment elements are shown in the results obtained demonstrate an adequate transition from the face-to-face model. One of the main strengths of this innovation refers to the successful migration to the digital model where the students expressed that the quality was comparable to the one they had in the face-to-face model. A lesson learned in this implementation was to assess the project by the students' voices, not as customers that needed to be satisfied but rather as partners that have to be interested in their learning process in order to succeed. For instance, the first configuration performed by the teacher received feedback from students which ended providing alternatives to make the setting up of the sesion easier. To achieve this, it is crucial that the teacher has presented clear objectives for the class, as these are discussed and clarified with the student there is an alignment of expectations on both stakeholders. One strategy is to ask students to read the course materials, before each class since it is key for students to have that previous knowledge to be able to interact. Although remote sessions were guided by the teacher with directed questions during this process, student preparation is important to the dynamic in a remote model.
A result of the assessment that was quickly recognized was the emotional and behavioral impact that the implementation was fulfilling. The COVID-19 pandemic has demanded an extra effort from teachers to deliver not only the excellence students are used to, but also to provide a bit of normality amid uncertainty and stress. To foster these learning environments educators need to continually assess their own performance to recognize the contribution of their teaching efforts, a skill that needs to be nurtured by faculty development programs even after the pandemic crisis has passed.
Students seem to respond well to active learning dynamics besides the distance. Some elements still require a continuous effort to impact on student engagement, for example in the resistance of participants towards opening their microphone to ask questions or share a comment, they still perceive that by making an oral contribution, it's an interruption of the teacher's explanation. There is a need to develop alternatives where all students can participate and engage in the most natural and effective way. This could be achieved by holding dedicated times for discussion, scheduling online forums or by making students work in small groups where they interact with their peers. Unfortunately, there is still not a way to emulate the totality of a face-to-face classroom and the live interaction within. However, these types of dynamics provide a sense of activeness and normality of the classes before the pandemic, with the elements that now make us nostalgic.
Traditionally, a large portion of teachers still limit themselves to conduct lecture-based sessions that are supported with Powerpoint presentations. This strategy is time consuming and could become tedious for Gen-Z students, who are used to receiving multiple stimuli and have shorter attention spans [7] . Implementing educational innovations allows that students stay engaged and active by collaborating in the sessions. Surely, this strategy requires that the teacher performs some additional work, due to the setup, planning and implementation. However, the results obtained make it worthwhile.
Aniela Mendez-Reguera: Investigation, Methodology, Writing – original draft, review & editing.
Mildred Lopez: Methodology; Formal analysis; Resources; Project administration; Roles/Writing - original draft, review and editing
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Aniela Mendez-Reguera is currently the Associate Director of the Medical program at Tecnologico de Monterrey, School of Medicine and Health Sciences, where she participates as Faculty in the immunology and microbiology courses. She is an M.D. with a Ph.D in Immunology. She has been enjoying life as an educator for the last four-years. Her contributions have taken her to participate in several international conferences in medicine and educational technology. Her educational innovation projects are published in the leading journals on medical education.
Mildred Lopez is the author of more than 40 articles and 11 book chapters. Currently, she is the Director of Educational Innovation at Tecnologico de Monterrey, School of Medicine and Health Sciences. Phd in Educational Innovation. Fellow of medical education at FAIMER Institute, and of the Association of Medical Education Europe (AMEE). Member of the Latin American Federation of Clinical Simulation and Patient Safety (FLASIC), and the National Academy of Medical Education in Mexico. Founding member of the Healthy Living for Pandemic Event Protection (HL - PIVOT) Network.
Editor: Dr. M. Malek
This paper is for special section VSI-tei. Reviews processed and recommended for publication by Guest Editor Dr. Samira Hosseini.
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ELIJAH L JOHN
Hserin Serin , Hamdi Serin
Recently there have been a growing number of researches on the influence of interactive whiteboard on engagement and achievement, and many of them have yielded positive results. Learner engagement has emerged from the connection between involvement and achievement and now quite many researchers agree on the correlation between learner engagement and learner achievement. This study aims to show whether the implementation of interactive whiteboard in Mathematics classes affect learner engagement and achievement. A questionnaire survey was conducted including 60 Mathematics department students at a private university in Iraq. The results indicated that the employment of interactive whiteboard impressively influences learners' engagement and achievement in Mathematics.
Recently technology-enhanced applications have become an increasingly important component of education. There is no doubt that the use of technology in education has yielded improvements in learners' achievement. Interactive whiteboard, regarded as one of the most effective educational tool, has the potential to revolutionize classroom instruction. Moreover, interactive whiteboard supports classroom management through motivating learners to participate in classroom activities. The use of interactive whiteboard enhances learners' engagement in the classroom which facilitates classroom management. This study focuses on the impacts interactive whiteboard makes on classroom management. A questionnaire including 100 participants was conducted and the study found that the use of interactive whiteboard in the classroom largely influences classroom management.
Human Systems Engineering and Design (IHSED2021) Future Trends and Applications
Omar Cóndor-Herrera
Nowadays, the teaching – learning process is characterized by the application and implementation of technological resources that look for the improvement of students´ attention and motivation within the educative process. In this context, one of those resources looking to accomplish this objective are digital whiteboards, that allow to make cooperative work, interact in audio and video in real time, closing the gap with the real world. The objective of this paper is to analyze the benefits that interactive whiteboards offer to the learning process, as well as to review previous studies that validate their application and the positive effects given to the educational process.
Journal of Educational Computing Research
Shu Ching Yang
In recent years, the interactive whiteboard (IWB) has been regarded as the most prominent information and communication technology auxiliary instruction device. It is touted as elevating the traditional teaching environment to a digital teaching environment because of its highly interactive features. The purpose of this study is to investigate elementary students’ perceptions of their teachers’ use of the different IWBs’ interactive functions in class and the effect of different IWB’s interactive uses on students’ learning attitudes. The findings showed that teachers’ use of the IWBs’ basic interactive function helps students develop positive learning attitudes, including enjoyment of learning, usefulness of IWB, and willingness to learn. However, such effects from the use of the IWB’s advanced interactive functions were not as clear, particularly the effects on usefulness of IWB and willingness to learn. Moreover, the students’ personal experience operating the IWB had an effect on...
R&E-SOURCE
Juraj Mistina
Modern electronic devices and teaching aids are constantly innovating education. Education has recently undergone many changes. Currently, the latest trend in the modernisation of teaching is represented by an interactive whiteboard. When used correctly, it represents a modern didactic tool that contributes to innovation and the efficiency of teaching a specific subject. This contribution aims to provide up-to-date information on using interactive whiteboards in secondary school teaching. In the paper, the authors describe the results of the conducted research. They focus on using the interactive whiteboard from the student’s point of view and the teacher’s. Using several research methods, the authors investigated the frequency of use of the interactive whiteboard and the learners’ opinions towards its use. They also investigated the possibilities of streamlining the teaching process.
Tạp chí Khoa học
Yehuda Peled , Mandy Medvin , Linda P Domanski
This research examines teacher attitudes and fears about interactive whiteboard (IWB) use as related to perceived classroom implementation to enhance student engagement and achievement. The research took place in four western PA school districts. Nearly 78 percent of all teachers surveyed reported using the IWB either "Often" or "All the time" and 75 percent of them reported using an IWB for two or more years. These combined data suggest that the districts in this study are investing in IWB technology and the majority of teachers are using IWBs on a regular basis for instruction. Number of years and frequency the IWB technology was used in the classroom was strongly related to levels of training and support the teachers believed they received, teachers' sense of self-efficacy and the perceived value of IWB technology as a useful tool, and teachers' perceptions about the positive effect that integrating IWBs had on student achievement.
keshav jadhav
Pooja Kumra
Globalization has permitted technical progress in communication field which enable users to access and exchange information at any time and from any place in the world. Technology has played a vital role in education field too. Learners of today are more familiar with technology and they are growing up in today's world which relies heavily on information technology. Information Technology (IT) has entered in teaching learning process too. When used effectively, IT can play a role in stimulating curiosity, interest and in facilitating and sustaining purposeful engagement in students. Moreover, technology can play a role in triggering and addressing personal, situational, and contextual factors that support autonomy, competence and enhance active and deep learning. Smart technologies are being used because they are said to " enhance learning " by increasing student " interest " through " active engagement ". Researchers have observed that Interactive White Board (IWB) or Smart Board technology affects all three forms of engagement (affective, participative, and cognitive). Since IWB's claims to increase student engagement, there is the possibility that it even increases student interests through the affective and participative domain. Moreover Interactive Whiteboard, regarded as one of the most effective educational tool, has the potential to revolutionize classroom instruction. IWB facilitates the teaching learning process and has the potential to provide a wide range of activities for learners. There is no doubt that use of technology in education has yielded improvements in learner's achievement, engagement, interest and retention. But on the other hand, Technology imposes few challenges too like incompetent and irrelevant e-content, lack of sufficient teacher's training, technical problems of hardware and software, lack of teacher's confidence and unavailability of proper technology.
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Tuyet Hayes
Omer Faruk Sozcu
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Dr. Saleena S Gil
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As A.I.-generated data becomes harder to detect, it’s increasingly likely to be ingested by future A.I., leading to worse results.
By Aatish Bhatia
Aatish Bhatia interviewed A.I. researchers, studied research papers and fed an A.I. system its own output.
The internet is becoming awash in words and images generated by artificial intelligence.
Sam Altman, OpenAI’s chief executive, wrote in February that the company generated about 100 billion words per day — a million novels’ worth of text, every day, an unknown share of which finds its way onto the internet.
A.I.-generated text may show up as a restaurant review, a dating profile or a social media post. And it may show up as a news article, too: NewsGuard, a group that tracks online misinformation, recently identified over a thousand websites that churn out error-prone A.I.-generated news articles .
In reality, with no foolproof methods to detect this kind of content, much will simply remain undetected.
All this A.I.-generated information can make it harder for us to know what’s real. And it also poses a problem for A.I. companies. As they trawl the web for new data to train their next models on — an increasingly challenging task — they’re likely to ingest some of their own A.I.-generated content, creating an unintentional feedback loop in which what was once the output from one A.I. becomes the input for another.
In the long run, this cycle may pose a threat to A.I. itself. Research has shown that when generative A.I. is trained on a lot of its own output, it can get a lot worse.
Here’s a simple illustration of what happens when an A.I. system is trained on its own output, over and over again:
This is part of a data set of 60,000 handwritten digits.
When we trained an A.I. to mimic those digits, its output looked like this.
This new set was made by an A.I. trained on the previous A.I.-generated digits. What happens if this process continues?
After 20 generations of training new A.I.s on their predecessors’ output, the digits blur and start to erode.
After 30 generations, they converge into a single shape.
While this is a simplified example, it illustrates a problem on the horizon.
Imagine a medical-advice chatbot that lists fewer diseases that match your symptoms, because it was trained on a narrower spectrum of medical knowledge generated by previous chatbots. Or an A.I. history tutor that ingests A.I.-generated propaganda and can no longer separate fact from fiction.
Just as a copy of a copy can drift away from the original, when generative A.I. is trained on its own content, its output can also drift away from reality, growing further apart from the original data that it was intended to imitate.
In a paper published last month in the journal Nature, a group of researchers in Britain and Canada showed how this process results in a narrower range of A.I. output over time — an early stage of what they called “model collapse.”
The eroding digits we just saw show this collapse. When untethered from human input, the A.I. output dropped in quality (the digits became blurry) and in diversity (they grew similar).
“6” | “8” | “9” | |
---|---|---|---|
Handwritten digits | |||
Initial A.I. output | |||
After 10 generations | |||
After 20 generations | |||
After 30 generations |
If only some of the training data were A.I.-generated, the decline would be slower or more subtle. But it would still occur, researchers say, unless the synthetic data was complemented with a lot of new, real data.
In one example, the researchers trained a large language model on its own sentences over and over again, asking it to complete the same prompt after each round.
When they asked the A.I. to complete a sentence that started with “To cook a turkey for Thanksgiving, you…,” at first, it responded like this:
Even at the outset, the A.I. “hallucinates.” But when the researchers further trained it on its own sentences, it got a lot worse…
After two generations, it started simply printing long lists.
And after four generations, it began to repeat phrases incoherently.
“The model becomes poisoned with its own projection of reality,” the researchers wrote of this phenomenon.
This problem isn’t just confined to text. Another team of researchers at Rice University studied what would happen when the kinds of A.I. that generate images are repeatedly trained on their own output — a problem that could already be occurring as A.I.-generated images flood the web.
They found that glitches and image artifacts started to build up in the A.I.’s output, eventually producing distorted images with wrinkled patterns and mangled fingers.
When A.I. image models are trained on their own output, they can produce distorted images, mangled fingers or strange patterns.
A.I.-generated images by Sina Alemohammad and others .
“You’re kind of drifting into parts of the space that are like a no-fly zone,” said Richard Baraniuk , a professor who led the research on A.I. image models.
The researchers found that the only way to stave off this problem was to ensure that the A.I. was also trained on a sufficient supply of new, real data.
While selfies are certainly not in short supply on the internet, there could be categories of images where A.I. output outnumbers genuine data, they said.
For example, A.I.-generated images in the style of van Gogh could outnumber actual photographs of van Gogh paintings in A.I.’s training data, and this may lead to errors and distortions down the road. (Early signs of this problem will be hard to detect because the leading A.I. models are closed to outside scrutiny, the researchers said.)
All of these problems arise because A.I.-generated data is often a poor substitute for the real thing.
This is sometimes easy to see, like when chatbots state absurd facts or when A.I.-generated hands have too many fingers.
But the differences that lead to model collapse aren’t necessarily obvious — and they can be difficult to detect.
When generative A.I. is “trained” on vast amounts of data, what’s really happening under the hood is that it is assembling a statistical distribution — a set of probabilities that predicts the next word in a sentence, or the pixels in a picture.
For example, when we trained an A.I. to imitate handwritten digits, its output could be arranged into a statistical distribution that looks like this:
Examples of initial A.I. output:
The distribution shown here is simplified for clarity.
The peak of this bell-shaped curve represents the most probable A.I. output — in this case, the most typical A.I.-generated digits. The tail ends describe output that is less common.
Notice that when the model was trained on human data, it had a healthy spread of possible outputs, which you can see in the width of the curve above.
But after it was trained on its own output, this is what happened to the curve:
It gets taller and narrower. As a result, the model becomes more and more likely to produce a smaller range of output, and the output can drift away from the original data.
Meanwhile, the tail ends of the curve — which contain the rare, unusual or surprising outcomes — fade away.
This is a telltale sign of model collapse: Rare data becomes even rarer.
If this process went unchecked, the curve would eventually become a spike:
This was when all of the digits became identical, and the model completely collapsed.
This doesn’t mean generative A.I. will grind to a halt anytime soon.
The companies that make these tools are aware of these problems, and they will notice if their A.I. systems start to deteriorate in quality.
But it may slow things down. As existing sources of data dry up or become contaminated with A.I. “ slop ,” researchers say it makes it harder for newcomers to compete.
A.I.-generated words and images are already beginning to flood social media and the wider web . They’re even hiding in some of the data sets used to train A.I., the Rice researchers found .
“The web is becoming increasingly a dangerous place to look for your data,” said Sina Alemohammad , a graduate student at Rice who studied how A.I. contamination affects image models.
Big players will be affected, too. Computer scientists at N.Y.U. found that when there is a lot of A.I.-generated content in the training data, it takes more computing power to train A.I. — which translates into more energy and more money.
“Models won’t scale anymore as they should be scaling,” said Julia Kempe , the N.Y.U. professor who led this work.
The leading A.I. models already cost tens to hundreds of millions of dollars to train, and they consume staggering amounts of energy , so this can be a sizable problem.
Finally, there’s another threat posed by even the early stages of collapse: an erosion of diversity.
And it’s an outcome that could become more likely as companies try to avoid the glitches and “ hallucinations ” that often occur with A.I. data.
This is easiest to see when the data matches a form of diversity that we can visually recognize — people’s faces:
A.I. images generated by Sina Alemohammad and others .
This set of A.I. faces was created by the same Rice researchers who produced the distorted faces above. This time, they tweaked the model to avoid visual glitches.
This is the output after they trained a new A.I. on the previous set of faces. At first glance, it may seem like the model changes worked: The glitches are gone.
After two generations …
After three generations …
After four generations, the faces all appeared to converge.
This drop in diversity is “a hidden danger,” Mr. Alemohammad said. “You might just ignore it and then you don’t understand it until it's too late.”
Just as with the digits, the changes are clearest when most of the data is A.I.-generated. With a more realistic mix of real and synthetic data, the decline would be more gradual.
But the problem is relevant to the real world, the researchers said, and will inevitably occur unless A.I. companies go out of their way to avoid their own output.
Related research shows that when A.I. language models are trained on their own words, their vocabulary shrinks and their sentences become less varied in their grammatical structure — a loss of “ linguistic diversity .”
And studies have found that this process can amplify biases in the data and is more likely to erase data pertaining to minorities .
Perhaps the biggest takeaway of this research is that high-quality, diverse data is valuable and hard for computers to emulate.
One solution, then, is for A.I. companies to pay for this data instead of scooping it up from the internet , ensuring both human origin and high quality.
OpenAI and Google have made deals with some publishers or websites to use their data to improve A.I. (The New York Times sued OpenAI and Microsoft last year, alleging copyright infringement. OpenAI and Microsoft say their use of the content is considered fair use under copyright law.)
Better ways to detect A.I. output would also help mitigate these problems.
Google and OpenAI are working on A.I. “ watermarking ” tools, which introduce hidden patterns that can be used to identify A.I.-generated images and text.
But watermarking text is challenging , researchers say, because these watermarks can’t always be reliably detected and can easily be subverted (they may not survive being translated into another language, for example).
A.I. slop is not the only reason that companies may need to be wary of synthetic data. Another problem is that there are only so many words on the internet.
Some experts estimate that the largest A.I. models have been trained on a few percent of the available pool of text on the internet. They project that these models may run out of public data to sustain their current pace of growth within a decade.
“These models are so enormous that the entire internet of images or conversations is somehow close to being not enough,” Professor Baraniuk said.
To meet their growing data needs, some companies are considering using today’s A.I. models to generate data to train tomorrow’s models . But researchers say this can lead to unintended consequences (such as the drop in quality or diversity that we saw above).
There are certain contexts where synthetic data can help A.I.s learn — for example, when output from a larger A.I. model is used to train a smaller one, or when the correct answer can be verified, like the solution to a math problem or the best strategies in games like chess or Go .
And new research suggests that when humans curate synthetic data (for example, by ranking A.I. answers and choosing the best one), it can alleviate some of the problems of collapse.
Companies are already spending a lot on curating data, Professor Kempe said, and she believes this will become even more important as they learn about the problems of synthetic data.
But for now, there’s no replacement for the real thing.
About the data
To produce the images of A.I.-generated digits, we followed a procedure outlined by researchers . We first trained a type of a neural network known as a variational autoencoder using a standard data set of 60,000 handwritten digits .
We then trained a new neural network using only the A.I.-generated digits produced by the previous neural network, and repeated this process in a loop 30 times.
To create the statistical distributions of A.I. output, we used each generation’s neural network to create 10,000 drawings of digits. We then used the first neural network (the one that was trained on the original handwritten digits) to encode these drawings as a set of numbers, known as a “ latent space ” encoding. This allowed us to quantitatively compare the output of different generations of neural networks. For simplicity, we used the average value of this latent space encoding to generate the statistical distributions shown in the article.
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the usage of digital interactive whiteboards is more efficient than traditional schooling, since. learning with a digital interactive whiteboard makes easier to students to acquire new. knowledge ...
The data were collected through the "Interactive Whiteboard Student Survey" (IWSS) developed by Türel (2011). The IWSS consisted of twenty-six 5-point Likert-type items. testing three factors - perceived efficiency of IWBs, perceived learning contribution and motivation, and the perceived negative effects of IWBs.
The interactive whiteboard-based instruction is a teaching approach where an interactive whiteboard (IWB, or electronic whiteboard or smartboard) functions as an all-in-one teaching tool. ... Search calls for papers Journal Suggester Open access publishing ... Yinghui Shi a National Engineering Research Centre for E-Learning, Central China ...
During these sessions, students and teachers interacted to co-construct ideas and socialize learning. The objective of this study was to assess the impact of introducing a digital whiteboard in student engagement. The quantitative approach integrated student's perception through an online survey with 12 items. The results show that the students ...
When used in a sensible way, Interactive Whiteboards (IWB) are supposed to motivate and engage students in learning in the classroom. Thereby, they might also stimulate students who are usually more restrained, such as more anxious students. However, the body of research on the impact of IWB lessons is rather small. The present study investigated whether a 45-minute lesson with the IWB ...
Many K-12 and higher-ed schools in both the United States and the United Kingdom have made a substantial investment in interactive whiteboard technology. ... In this study a literature review was conducted to better understand the research to date in this area. Several common themes surfaced including the effect of IWBs on pedagogy, motivation ...
Relevant studies on interactive whiteboard-supported learning. Research has highlighted the benefits of technology-supported collaborative writing in terms of text quality. ... The teacher in the TW condition also displayed one or two papers using the main computer, whereas the teacher in the CG verbally explained one or two papers ...
This research project was undertaken as follow-up to a prior project that found the majority of instructors at a small college used interactive whiteboard (IWB) software in less than one-quarter ...
The Impact of Interactive Whiteboards on Education. Yinghui Shi 1, Zongkai Yang1, Harrison Hao Yang1,2, Sanya Liu1. National Engineering Research Center for E-Learning, Central China Normal ...
Far from calling into question the need to integrate technology into education, the results reveal that certain tools, such as the IWB, may be more complicated and time-consuming to integrate than others. Over the past five years, the interactive whiteboard (IWB) has been massively introduced into schools across the province of Quebec, Canada. This study explores how the IWB is being used, and ...
This study examines key ideas, evidences, and works of interactive whiteboards on education over the ten-year period from 2002 to 2011. It focuses on seven research hotspots and priority areas: teaching strategies and methods, instructional effectiveness, technology diffusion and infusion, users, mathematics education, science education in primary schools, language teaching and learning.
The interactive whiteboards, pedagogy and pupil performance evaluation: An evaluation of the schools whiteboard expansion (SWE) project: London challenge. Research report no. 816, Institute of Education, University of London.
Abstract. Interactive white boards (IWBs) have been heralded by many as a valuable teaching tool offering innumerable opportunities for increasing student engagement and learning (Campbell & Kent, 2010; Glover, Miller, Averis, & Door, 2005). Although research clearly shows IWBs have the potential to transform the way in which teachers teach ...
Most of the experimental research was used to determine student achievement and attitude toward interactive whiteboards in the class; however, they concentrated more on the subjects of English as ...
William D. Beeland, Jr. Abstract: The purpose of this action research study was to determine the effect of the use of interactive whiteboards as an instructional tool on student engagement. Specifically, the desire was to see if student engagement in the learning process is increased while using an interactive whiteboard to deliver instruction.
During these sessions, students and teachers interacted to co-construct ideas and socialize learning. The objective of this study was to assess the impact of introducing a digital whiteboard in student engagement. The quantitative approach integrated student's perception through an online survey with 12 items. The results show that the students ...
Academia.edu is a platform for academics to share research papers. Investigating the Effects of Interactive Whiteboards on Student Achievement ... The focus of this study of the literature is on the latest display resource for instructors, the Interactive Whiteboard (IWB), a device that allows for seamless transitions from visual to aural to ...
Lieven Verschaffel (1957) obtained in 1984 the degree of Doctor in Educational Sciences at the University of Leuven, Belgium. From 1979 until 2000 he fulfilled several research positions at the Fund for Scientific Research-Flanders. Since 2000 he is a full professor in educational sciences of that same university.
The purpose of this paper is to analyze of latest research focused on the investigation of interactive. whiteboards used in teaching and learning Science. In the theoretical framework the main objectives are: a) the identification of specific research regarding the integration of interactive whiteboards in teaching.
This comparative research paper contrasts the use of interactive whiteboards (IWB) and older models of teaching (OMT) to examine the effect of both upon children's learning attitude and their ...
Specifically, the following research ques tions were addressed: 1. Does the use of an interactive whiteboard as an instructional tool affect student engagement? 2. Does the method in which an interactive whiteboard is used as an instructional tool in the classroom affect the degree to which students are engaged?
Aatish Bhatia interviewed A.I. researchers, studied research papers and fed an A.I. system its own output. Aug. 25, 2024 The internet is becoming awash in words and images generated by artificial ...
1. Interactive Whiteboards: The use of digital interactive whiteboards (IWBs) in teaching and learning has been widely embraced due to their ability to engage students, facilitate collaboration ...
The research uses Interactive Whiteboard Attitude Survey, observation skill card for using Interactive Whiteboard in the classrooms and structured interviews with students. ... This paper will ...