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32
Software Engineering / Re: Stack Overflow looks at programming languages used at night - See more at: http:
« on: April 27, 2017, 09:18:31 AM »
Good post madam 
33
Software Engineering / Re: উবারের পর ঢাকায় এবার চালু হলো নৌকা সার্ভিস 'কুবের'
« on: April 27, 2017, 09:17:49 AM »
Seriously !!!!!!!!
34
Software Engineering / Re: Why NoSQL Database?
« on: April 27, 2017, 09:17:02 AM »
Really helpful post..thanks for the post madam
35
Software Engineering / Re: 3-D-printable implants for damaged knees
« on: April 25, 2017, 08:55:33 AM »
Informative one
36
Software Engineering / Agricultural technological innovation
« on: April 25, 2017, 08:52:23 AM »
There are an estimated 570 million farms in the world and, in a neat twist of number synergy, according to Valoral Advisors, funding rounds in technological innovations along the agriculture and food value chain also raised around $570 million in 2014.
While much of this investment is directed at ag-tech startups and disruptive market newcomers, in many ways priorities remain the same as ever – innovation in resource use, especially in terms of land and water (also energy), to boost efficiency and yields. Here are five of the solutions helping to support global growth of sustainable agriculture and food production…
1. DATA PRESERVED IN SOIL
Data preserved in soil
For traditional farming models, perhaps the primary determinant of supply capacity is simply the availability and suitability of land. However, any idea of future potential must be built on current data, with what data there is then mapped to tell the story of a region. This story is effectively written in the dirt, the soil.
The Africa Soil Information Service (AfSIS) is developing continent-wide digital soil maps for sub-Saharan Africa using new analysis, statistics, field trials and crowdsourcing. Funded by the Bill and Melinda Gates Foundation, the ISRIC World Soil Information AfSIS project has forged key partnerships with governments, plus a range of stakeholders and academic institutions, including the Earth Institute at Columbia University.
Digital soil mapping, especially in data-sparse regions such as Africa, is key to planning sustainable agricultural intensification and natural resources management. With open access, these interactive maps are publicly available to be explored on Google Earth.
2. LETTUCE WITH YOUR MICROCHIPS?
Lettuce with your microchips
Singapore relies heavily on imports for more than 90 per cent of its fruit and vegetables. Therefore, diners in Japanese restaurants there might be surprised to discover their rocket, radish and baby spinach has not only been cultivated locally in the country’s first licensed indoor vegetable farm, but by an electronics giant better known for TVs – Panasonic.
Annual soil-based production capacity at the initial Panasonic facility launched last year was 3.6 tonnes, but the company is by no means the only high-tech brand setting up urban and vertical farms, to showcase technology rather than make profit.
Sharp is growing strawberries in Dubai, while Sony, Toshiba and Fujitsu are all utilising former clean-room facilities at semiconductor plants across Japan for lettuce. These no-wash, no-soil greens are cultivated by means of hydroponics and grown at more than twice the speed of normal field production, thanks to specialised LED lighting to optimise photosynthesis.
3. GREENS FED ON RAINBOW WASTE
Greens fed on rainbow waste
Hydroponics, as the name suggests, is a growing method based on use of mineral-enriched water, whereas aquaponics takes matters a step further, bringing together fish and plant farming in one recirculating system.
At Bioaqua Farm at Blackford in Somerset – the largest integrated aquaponic farm in Europe – vegetables are grown and Rainbow Trout reared together in organic symbiosis, without chemicals or pesticides, but with the help of bees and worms.
The fish provide most of the plant nutrition, by way of aquaculture effluent. In turn, fish waste metabolites are removed by nitrification and direct uptake by plants, with the suitably treated water then flowing back to the fish. In all, it is claimed this virtuous circle of reciprocity requires up to 95 per cent less water than traditional horticulture farming.
For sustainable food production and agriculture, the aquaponics ecosystem principles also appear attractively scalable, from back gardens to commercial facilities.
4. POWER OF A NO-SALT DIET
Power of a no-salt diet
Water efficiency in farming and food production, whether for traditional rural irrigation, arid regions or urban farms, represents a key metric in the face of global population growth and climate change.
Considered together, scarcity of freshwater resources and the fact that 71 per cent of the Earth’s surface is nevertheless covered in water, therefore make a compelling argument for desalination. The stumbling block, historically, has been its energy-hungry nature and prohibitively high running costs relative to agricultural profit margins.
The innovative solution offered by Sundrop Farms draws on one of the few renewable resources in even more abundant supply than seawater – sunlight. Sundrop Farms harvests solar power to generate energy for desalination to supply hydroponic greenhouses.
Requiring no freshwater, farmland or fossil fuels, this potential game-changer for sustainable farming is creating 300 jobs in Port Augusta, South Australia, with a ten-year contract won to grow tomatoes for Coles supermarkets.
5. SIDE ORDER OF WINGS
Side order of wings
In the media, drones have mostly been associated with the military and spying, plus the odd pizza-delivery publicity stunt.
An annual competition in the United Arab Emirates, UAE Drones for Good Award, acknowledges both this dark reputation and that things are changing. Competition finalists this year pitched benefits for unmanned aerial vehicles from conservation support to medical deliveries, as well as farming help.
The Munich-based Quantum-Systems entry was a transition aircraft combining capabilities of a multicopter and fixed-wing model – vertical take-off, plus fast forward flight like a normal plane. Quantum VRT design allows farmers to adopt precise fertilisation strategies via accurate flight-planning software with evaluation of crop conditions, so reducing reliance on fertilisers and boosting yields.
Dubai plans to scale up agriculture drone technology usage in a bid to become self-sufficient in food security by 2030. With 98 per cent imports, the emirate currently outstrips Singapore.
While much of this investment is directed at ag-tech startups and disruptive market newcomers, in many ways priorities remain the same as ever – innovation in resource use, especially in terms of land and water (also energy), to boost efficiency and yields. Here are five of the solutions helping to support global growth of sustainable agriculture and food production…
1. DATA PRESERVED IN SOIL
Data preserved in soil
For traditional farming models, perhaps the primary determinant of supply capacity is simply the availability and suitability of land. However, any idea of future potential must be built on current data, with what data there is then mapped to tell the story of a region. This story is effectively written in the dirt, the soil.
The Africa Soil Information Service (AfSIS) is developing continent-wide digital soil maps for sub-Saharan Africa using new analysis, statistics, field trials and crowdsourcing. Funded by the Bill and Melinda Gates Foundation, the ISRIC World Soil Information AfSIS project has forged key partnerships with governments, plus a range of stakeholders and academic institutions, including the Earth Institute at Columbia University.
Digital soil mapping, especially in data-sparse regions such as Africa, is key to planning sustainable agricultural intensification and natural resources management. With open access, these interactive maps are publicly available to be explored on Google Earth.
2. LETTUCE WITH YOUR MICROCHIPS?
Lettuce with your microchips
Singapore relies heavily on imports for more than 90 per cent of its fruit and vegetables. Therefore, diners in Japanese restaurants there might be surprised to discover their rocket, radish and baby spinach has not only been cultivated locally in the country’s first licensed indoor vegetable farm, but by an electronics giant better known for TVs – Panasonic.
Annual soil-based production capacity at the initial Panasonic facility launched last year was 3.6 tonnes, but the company is by no means the only high-tech brand setting up urban and vertical farms, to showcase technology rather than make profit.
Sharp is growing strawberries in Dubai, while Sony, Toshiba and Fujitsu are all utilising former clean-room facilities at semiconductor plants across Japan for lettuce. These no-wash, no-soil greens are cultivated by means of hydroponics and grown at more than twice the speed of normal field production, thanks to specialised LED lighting to optimise photosynthesis.
3. GREENS FED ON RAINBOW WASTE
Greens fed on rainbow waste
Hydroponics, as the name suggests, is a growing method based on use of mineral-enriched water, whereas aquaponics takes matters a step further, bringing together fish and plant farming in one recirculating system.
At Bioaqua Farm at Blackford in Somerset – the largest integrated aquaponic farm in Europe – vegetables are grown and Rainbow Trout reared together in organic symbiosis, without chemicals or pesticides, but with the help of bees and worms.
The fish provide most of the plant nutrition, by way of aquaculture effluent. In turn, fish waste metabolites are removed by nitrification and direct uptake by plants, with the suitably treated water then flowing back to the fish. In all, it is claimed this virtuous circle of reciprocity requires up to 95 per cent less water than traditional horticulture farming.
For sustainable food production and agriculture, the aquaponics ecosystem principles also appear attractively scalable, from back gardens to commercial facilities.
4. POWER OF A NO-SALT DIET
Power of a no-salt diet
Water efficiency in farming and food production, whether for traditional rural irrigation, arid regions or urban farms, represents a key metric in the face of global population growth and climate change.
Considered together, scarcity of freshwater resources and the fact that 71 per cent of the Earth’s surface is nevertheless covered in water, therefore make a compelling argument for desalination. The stumbling block, historically, has been its energy-hungry nature and prohibitively high running costs relative to agricultural profit margins.
The innovative solution offered by Sundrop Farms draws on one of the few renewable resources in even more abundant supply than seawater – sunlight. Sundrop Farms harvests solar power to generate energy for desalination to supply hydroponic greenhouses.
Requiring no freshwater, farmland or fossil fuels, this potential game-changer for sustainable farming is creating 300 jobs in Port Augusta, South Australia, with a ten-year contract won to grow tomatoes for Coles supermarkets.
5. SIDE ORDER OF WINGS
Side order of wings
In the media, drones have mostly been associated with the military and spying, plus the odd pizza-delivery publicity stunt.
An annual competition in the United Arab Emirates, UAE Drones for Good Award, acknowledges both this dark reputation and that things are changing. Competition finalists this year pitched benefits for unmanned aerial vehicles from conservation support to medical deliveries, as well as farming help.
The Munich-based Quantum-Systems entry was a transition aircraft combining capabilities of a multicopter and fixed-wing model – vertical take-off, plus fast forward flight like a normal plane. Quantum VRT design allows farmers to adopt precise fertilisation strategies via accurate flight-planning software with evaluation of crop conditions, so reducing reliance on fertilisers and boosting yields.
Dubai plans to scale up agriculture drone technology usage in a bid to become self-sufficient in food security by 2030. With 98 per cent imports, the emirate currently outstrips Singapore.
37
Software Engineering / Current trends in Machine Learning
« on: April 22, 2017, 09:17:38 AM »
Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?
2017 Machine Learning Trends in Research
In the research areas, Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning, Forbes predicts the theoretical research in ML will gradually pave the way for business problem solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.
2017 ML Application Development Trends
Gartner’s Top 10 Technology Trends for 2017 predicts that the combined AI and advanced ML practice that ignited about four years ago and since continued unscathed, will dominate Artificial Intelligence application development in 2017. This lethal combination will deliver more systems that “understand, learn, predict, adapt and potentially operate autonomously. “ Cheap hardware, cheap memory, cheap storage technologies, more processing power, superior algorithms, and massive data streams will all contribute to the success of ML-powered AI applications. There will be steady rise in Ml-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. For businesses that means more automated functions and fewer human checkpoints. 2017 Predictions from Forrester suggests that the Artificial Intelligence and Machine Learning Cloud will increasingly feed on IoT data as sensors and smart apps take over every facet of our daily lives.
Democratization of Machine Learning in the Cloud
Democratization of AI and ML through Cloud technologies, open standards, and algorithm economy will continue. The growing trend of deploying prebuilt ML algorithms to enable Self-Service Business Intelligence and Analytics is a positive step towards democratization of ML. In Google Says Machine Learning is the Future, the author champions the democratization of ML through idea sharing. A case in point is Google’s Tensor Flow, which has championed the need for open standards in Machine Learning. This article claims that almost anyone with a laptop and an Internet connection can dare to be a Machine Learning expert today provided they have the right mind set.
The provisioning of Cloud-based IT services was already a good step to make advanced Data Science a mainstream activity, and now with Cloud and packaged algorithms, mid-sized ad smaller businesses will have access to Self-Service BI and Analytics, which was till now only a dream. Also, the mainstream business users will gradually take an active role in data-centric business systems. Machine Learning Trends – Future AI claims that more enterprises in 2017 will capitalize on the Machine Learning Cloud and do their part to lobby for democratized data technologies.
Platform Wars will Peak in 2017
The platform war between IBM, Microsoft, Google, and Facebook to be the leader in ML developments will peak in 2017. Where Machine Learning Is Headed, predicts that 2017 will experience a tremendous growth of smart apps, digital assistants, and main-stream use of Artificial Intelligence. Although many ML-enabled AI systems have turned into success stories, the self driving cars may die a premature death.
Humans will Make Peace with Machines
Since 2012 the global business community has witnessed a meteoric rise and widespread proliferation of data technologies. Finally, humans will realize that it is time to stop fearing the machines and begin working with them. The InfoWorld article titled Application Development, Docker, Machine Learning Are Top Tech Trends for 2017 asserts humans and machines will work with each other, not against each other. In this context, readers should review the DATAVERSITY® article The Future of Machine Learning: Trends, Observations, and Forecasts, where the readers are reminded that as businesses develop a strong dependence on pre-built ML algorithms for Advanced Analytics, the need for Data Scientists or large IT departments may diminish.
Demand-Supply Gaps in Data Science and Machine Learning will Rise
The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom, and will focus more on practical skills rather than on theoretical knowledge. KDNuggets will continue to challenge the curious mind by publishing articles like 10 Algorithms that Machine Learning Engineers Should Know . 2017 will witness a steady rise in contributions from KDNugget and Kaggle in providing alternative training to future Data Scientists and Machine Learning experts through practical skill development.
The Algorithm Economy will take Center Stage
Over the next year or two, businesses will be using canned algorithms for all data-centric activities like BI, Predictive Analytics, and CRM. The algorithm economy, which Forbes mentions, will usher in a marketplace where all data companies will compete for a space. In 2017, global businesses will engage in Self-Service BI, and experience the growth of algorithmic business solutions, and ML in the Cloud. So far as algorithm-driven business decision making is concerned, 2017 may actually see two distinct types of algorithm economies. On one hand, average businesses will utilize canned algorithmic models for their operational and customer-facing functions. On the other hand, proprietary ML algorithms will become a market differentiator among large, competing enterprises.
Some Thoughts to Ponder
If the threat of intelligent machines taking over Data Scientists is really as real as it is made out to be, then 2017 is probably the year when the global Data Science community should take a new look at the capabilities of so-called “smart machines.” The repeated failure of autonomous cars has made one point clear – that even learning machines cannot surpass the natural thinking faculties bestowed by nature on human beings. If autonomous or self-guided machines have to be useful to human society, then the current Artificial Intelligence and Machine Learning research should focus on acknowledging the limits of machine power and assign tasks that are suitable for the machines and include more human interventions at necessary checkpoints to avert disasters. Repetitive, routine tasks can be well handled by machines, but any out-of-the-ordinary situations will still require human intervention.
Source: DATAVERSITY
2017 Machine Learning Trends in Research
In the research areas, Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning, Forbes predicts the theoretical research in ML will gradually pave the way for business problem solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.
2017 ML Application Development Trends
Gartner’s Top 10 Technology Trends for 2017 predicts that the combined AI and advanced ML practice that ignited about four years ago and since continued unscathed, will dominate Artificial Intelligence application development in 2017. This lethal combination will deliver more systems that “understand, learn, predict, adapt and potentially operate autonomously. “ Cheap hardware, cheap memory, cheap storage technologies, more processing power, superior algorithms, and massive data streams will all contribute to the success of ML-powered AI applications. There will be steady rise in Ml-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. For businesses that means more automated functions and fewer human checkpoints. 2017 Predictions from Forrester suggests that the Artificial Intelligence and Machine Learning Cloud will increasingly feed on IoT data as sensors and smart apps take over every facet of our daily lives.
Democratization of Machine Learning in the Cloud
Democratization of AI and ML through Cloud technologies, open standards, and algorithm economy will continue. The growing trend of deploying prebuilt ML algorithms to enable Self-Service Business Intelligence and Analytics is a positive step towards democratization of ML. In Google Says Machine Learning is the Future, the author champions the democratization of ML through idea sharing. A case in point is Google’s Tensor Flow, which has championed the need for open standards in Machine Learning. This article claims that almost anyone with a laptop and an Internet connection can dare to be a Machine Learning expert today provided they have the right mind set.
The provisioning of Cloud-based IT services was already a good step to make advanced Data Science a mainstream activity, and now with Cloud and packaged algorithms, mid-sized ad smaller businesses will have access to Self-Service BI and Analytics, which was till now only a dream. Also, the mainstream business users will gradually take an active role in data-centric business systems. Machine Learning Trends – Future AI claims that more enterprises in 2017 will capitalize on the Machine Learning Cloud and do their part to lobby for democratized data technologies.
Platform Wars will Peak in 2017
The platform war between IBM, Microsoft, Google, and Facebook to be the leader in ML developments will peak in 2017. Where Machine Learning Is Headed, predicts that 2017 will experience a tremendous growth of smart apps, digital assistants, and main-stream use of Artificial Intelligence. Although many ML-enabled AI systems have turned into success stories, the self driving cars may die a premature death.
Humans will Make Peace with Machines
Since 2012 the global business community has witnessed a meteoric rise and widespread proliferation of data technologies. Finally, humans will realize that it is time to stop fearing the machines and begin working with them. The InfoWorld article titled Application Development, Docker, Machine Learning Are Top Tech Trends for 2017 asserts humans and machines will work with each other, not against each other. In this context, readers should review the DATAVERSITY® article The Future of Machine Learning: Trends, Observations, and Forecasts, where the readers are reminded that as businesses develop a strong dependence on pre-built ML algorithms for Advanced Analytics, the need for Data Scientists or large IT departments may diminish.
Demand-Supply Gaps in Data Science and Machine Learning will Rise
The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom, and will focus more on practical skills rather than on theoretical knowledge. KDNuggets will continue to challenge the curious mind by publishing articles like 10 Algorithms that Machine Learning Engineers Should Know . 2017 will witness a steady rise in contributions from KDNugget and Kaggle in providing alternative training to future Data Scientists and Machine Learning experts through practical skill development.
The Algorithm Economy will take Center Stage
Over the next year or two, businesses will be using canned algorithms for all data-centric activities like BI, Predictive Analytics, and CRM. The algorithm economy, which Forbes mentions, will usher in a marketplace where all data companies will compete for a space. In 2017, global businesses will engage in Self-Service BI, and experience the growth of algorithmic business solutions, and ML in the Cloud. So far as algorithm-driven business decision making is concerned, 2017 may actually see two distinct types of algorithm economies. On one hand, average businesses will utilize canned algorithmic models for their operational and customer-facing functions. On the other hand, proprietary ML algorithms will become a market differentiator among large, competing enterprises.
Some Thoughts to Ponder
If the threat of intelligent machines taking over Data Scientists is really as real as it is made out to be, then 2017 is probably the year when the global Data Science community should take a new look at the capabilities of so-called “smart machines.” The repeated failure of autonomous cars has made one point clear – that even learning machines cannot surpass the natural thinking faculties bestowed by nature on human beings. If autonomous or self-guided machines have to be useful to human society, then the current Artificial Intelligence and Machine Learning research should focus on acknowledging the limits of machine power and assign tasks that are suitable for the machines and include more human interventions at necessary checkpoints to avert disasters. Repetitive, routine tasks can be well handled by machines, but any out-of-the-ordinary situations will still require human intervention.
Source: DATAVERSITY
38
Software Engineering / Future of artificial intelligence
« on: April 22, 2017, 07:03:12 AM »
This following article really amazed me.
Source: http://www.businessinsider.com/researchers-predictions-future-artificial-intelligence-2015-10/#pieter-abbeel-says-robots-will-keep-us-safer-especially-from-disasters-1
Source: http://www.businessinsider.com/researchers-predictions-future-artificial-intelligence-2015-10/#pieter-abbeel-says-robots-will-keep-us-safer-especially-from-disasters-1
39
Science and Information / Technological trends those are going to rule in 2017
« on: April 22, 2017, 06:58:16 AM »
Eagerly waiting to see the outcome..
Source: https://www.forbes.com/sites/jaysondemers/2016/11/16/7-technology-trends-that-will-dominate-2017/#720012d34a51
Source: https://www.forbes.com/sites/jaysondemers/2016/11/16/7-technology-trends-that-will-dominate-2017/#720012d34a51
40
Teaching & Research Forum / Re: শিক্ষক কি শুধুই শিক্ষক!
« on: April 22, 2017, 06:47:29 AM »
Wonderful post..Thanks for sharing sir..
41
Teaching & Research Forum / Re: How to Use Google Classroom Efficiently?
« on: April 22, 2017, 06:46:01 AM »
Informative and effective post madam..
42
Software Engineering / Re: Aggregate and Distributional Effects of Financial Globalization
« on: April 20, 2017, 11:34:31 AM »
Informative post
43
Software Engineering / Technology, how is it a problem in education today??
« on: April 20, 2017, 11:31:22 AM »
Technology has been used in most schools since the ‘90s (remember CD-ROMS?), but it still has some hiccups.
The crutch
Students are so quick to turn to the Internet to answer questions that some believe critical thinking has gone down the tube. Spelling is no longer something tested if everything is autocorrected and spell checked. This may be a larger issue of technology on our memory and brain-strength, but if we are using the Internet in schools, then kids are being taught to use Google to answer all their questions and to essentially, copy and paste their knowledge. Education needs to figure out how to use technology in a way that doesn’t replace knowledge, but reinforces it. Yet for students with disabilities or language barriers, using technology in the classroom can be less of a crutch and more of a launchpad for understanding.
The crash
Before it was the dog ate the homework, now it’s the computer crashed and “It was all done before it got erased!” But, this popular excuse is used because it does happen. When using the computer and all its glitches to create a project that requires hours of work, it sometimes gets erased, doesn’t transfer over correctly, doesn’t save, or for one human error or another is gone. Many technology rookies have been in this position and curse at the computer that has stolen hours. Some students struggle simply to complete work that it seems unfair to put obstacles in their way, especially when some students may not have programs or the technology at home to become familiar with it. The problem with technology glitches is also seen with online textbooks. Some students have issues accessing textbooks at home if they don’t have a large enough bandwidth. Other access problems to online materials can delay students and put them behind in class. This is one of many reasons to make sure your school has a stable, reliable cloud storage system in place.
The old-timer
Some teachers do not utilize the technology they’ve been given. They have been teaching for years and don’t want to incorporate something new into their time-tested lesson plans. Some schools are pushing instructors to incorporate technology into their syllabi and when it is poorly taught the technology is not used at optimal level. Any teacher given high-tech programs and expected to teach it in the classroom deserves proper training, and sometimes it isn’t provided. But all hope is not lost. We interviewed a few college students, and they had some helpful input for teachers to improve their use of technology in the classroom.
The Facebook
And Twitter, Instagram, Pinterest, YouTube, etcetera. Putting a computer in front of a high school student and expecting them NOT to go on Facebook or any other distracting non-school related site is kind of a joke. And it isn’t just the younger students that are in danger of losing focus; even graduate students can hardly help themselves to online distractions in the classroom. When keeping students excited and focused on the lesson at hand is one of the hardest task a teacher faces, a computer can be one of the most detrimental things to that student’s learning. Unless, of course, they’re using Facebook for collaboration.
The Band-Aid
The idea that technology can save education may have some truth in it, but it may be problematic to treat all our educational issues with technology. In 2007, Education Week reported on a major federal study that found, “no difference in academic achievement between students who used the technology in their classrooms and youngsters who used other methods.” If students aren’t proficient in their studies to begin with and technology is used incorrectly, a whole mess of problems could arise. What’s wrong with the Band-Aid thinking is that technology needs to be planned out into schools in a very precise manner in order for it to be effective, and to cover all of education’s problems in a Band-Aid may further aggravate the issues.
Source:edtechtimes
The crutch
Students are so quick to turn to the Internet to answer questions that some believe critical thinking has gone down the tube. Spelling is no longer something tested if everything is autocorrected and spell checked. This may be a larger issue of technology on our memory and brain-strength, but if we are using the Internet in schools, then kids are being taught to use Google to answer all their questions and to essentially, copy and paste their knowledge. Education needs to figure out how to use technology in a way that doesn’t replace knowledge, but reinforces it. Yet for students with disabilities or language barriers, using technology in the classroom can be less of a crutch and more of a launchpad for understanding.
The crash
Before it was the dog ate the homework, now it’s the computer crashed and “It was all done before it got erased!” But, this popular excuse is used because it does happen. When using the computer and all its glitches to create a project that requires hours of work, it sometimes gets erased, doesn’t transfer over correctly, doesn’t save, or for one human error or another is gone. Many technology rookies have been in this position and curse at the computer that has stolen hours. Some students struggle simply to complete work that it seems unfair to put obstacles in their way, especially when some students may not have programs or the technology at home to become familiar with it. The problem with technology glitches is also seen with online textbooks. Some students have issues accessing textbooks at home if they don’t have a large enough bandwidth. Other access problems to online materials can delay students and put them behind in class. This is one of many reasons to make sure your school has a stable, reliable cloud storage system in place.
The old-timer
Some teachers do not utilize the technology they’ve been given. They have been teaching for years and don’t want to incorporate something new into their time-tested lesson plans. Some schools are pushing instructors to incorporate technology into their syllabi and when it is poorly taught the technology is not used at optimal level. Any teacher given high-tech programs and expected to teach it in the classroom deserves proper training, and sometimes it isn’t provided. But all hope is not lost. We interviewed a few college students, and they had some helpful input for teachers to improve their use of technology in the classroom.
The Facebook
And Twitter, Instagram, Pinterest, YouTube, etcetera. Putting a computer in front of a high school student and expecting them NOT to go on Facebook or any other distracting non-school related site is kind of a joke. And it isn’t just the younger students that are in danger of losing focus; even graduate students can hardly help themselves to online distractions in the classroom. When keeping students excited and focused on the lesson at hand is one of the hardest task a teacher faces, a computer can be one of the most detrimental things to that student’s learning. Unless, of course, they’re using Facebook for collaboration.
The Band-Aid
The idea that technology can save education may have some truth in it, but it may be problematic to treat all our educational issues with technology. In 2007, Education Week reported on a major federal study that found, “no difference in academic achievement between students who used the technology in their classrooms and youngsters who used other methods.” If students aren’t proficient in their studies to begin with and technology is used incorrectly, a whole mess of problems could arise. What’s wrong with the Band-Aid thinking is that technology needs to be planned out into schools in a very precise manner in order for it to be effective, and to cover all of education’s problems in a Band-Aid may further aggravate the issues.
Source:edtechtimes