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Saturday, January 27, 2024

Smart village

From Wikipedia, the free encyclopedia

Concept of smart villages is a global modern approach for off-grid communities. Vision behind this concept is to assist the policy makers, donors and socio-economic planner for rural electrification worldwide.

The concept has received much attention in the context of Asian and African countries, although it is also found in other parts of the world such as Europe. Smart villages concept is engaged in efforts to combat the real barriers to energy access in villages, particularly in developing countries with technological, financial and educational methodology. A major focus of smart villages is the adoption of renewable resource in place of fossil fuel, which is seen as the best approach that can be developed through off-grid systems or communities.

Off-grid systems and off-grid communities

The term “Off-grid” itself is very broad and simply refers to "not using or depending on electricity provided through main or national grids and generated by main power infrastructures. The term is also used to describe a particular lifestyle which is embodied by autonomous structures. Off-grid systems have a semi or autonomous capability to satisfy electricity demand through local power generation. The term off-grid systems cover both mini-grids for serving multiple users and stand-alone systems for individual appliance or users. In spite witnessing use of fossil fuel for power generation by mini or individual off-grid system, it is broadly defined that off-grid systems are actually based on renewable energy resources. The terms "micro-grid, nano-grid and pico-grid are used to differentiate different kinds of mini-grids with size thresholds under off-grid approach.

Access to un-interrupted and low cost electricity for socio-economic development is an important requirement. There is a universal demand of grid-based and off-grid solutions to ensure access to electricity all over the world, without off-grid approach increasing demand and decreasing supply cannot be stabilized for the mankind on this planet.

About 80% of world's population live in rural areas and majority of these people do not have access to electricity. Due to lack of employment people from rural areas migrate to urban areas where they find employment opportunities much easily because of industrial infrastructure established primarily on availability of electricity. International Renewable Energy Agency (IRENA) power generation projects based on renewable energy technology at low cost are the attractive option for off-grid electrification in most of the rural areas of Asian countries. Its work will satisfy the rural electricity demand and provide employment opportunities to minimize the rapid urbanization.

ICT Village Model

The ICT Village model stems from the need to provide technologies and services to the most disadvantaged communities to enable them to promote their own development. The replicable model of ICT Village focuses on three types of intervention: i) ensuring an education to young people aimed at enhancing local resources and creating jobs; ii) ensuring a basic level of health; iii) providing internet access to the whole community to strengthen its capacity for socio-economic development.

The ICT Village model, developed and launched by OCCAM, The Observatory on Digital Communication has had a large echo, influencing deeply different levels of the society: the model has even been cited by the USSTRATCOM Global Innovation and Strategy Center in one of its document concerning the Village Infrastructure Kit-Alpha (VIKA).

ICT Village in Honduras: The Solar Village

The first ICT village project was carried out in 1999 in Honduras, hit by the devastating hurricane Mitch. With the support of UNESCO, the INFOPOVERTY PROGRAMME, the Organization of American States (OEA), the Ministry of Science and Technology (COHCIT) and the local University (UCyT) and the main international organization, it was possible to implement two projects initially called Solar Village in the communities of San Ramon and San Francisco de Lempira. Thanks to the use of solar panels and the first satellite equipped for the Internet of OnSatNet, the supply of electricity was guaranteed, and a connection to 108 mb / sec, a real record for the time, able to provide more than 30,000 people the first e-learning and telemedicine services provided, allowing the population to use these new technologies advantageously and to connect to the rest of the world through e-commerce and e-government initiatives.

ICT Village in Tunisia: Borji Ettouil

Presented and discussed in several Infopoverty World Conferences, held annually at the United Nations Headquarters in New York, the model is proposed to the Government of Tunisia for an experimentation in the village of Borji Ettouil at the WSIS Summit in November 2003. The success of this WSIS - ICT Village - supported by the National Solidarity Fund and visited by numerous government delegations and personalities, who appreciated the operational applications of telemedicine, e-learning and internet community access - allows validating their effectiveness and opens the doors to numerous invitations to replicate it over the years in various countries, including Peru, Ethiopia, Dominican Republic, Lesotho, Tunisia, Ghana, South Lebanon, Navajo Nation, Madagascar.

ICT Village in Lebanon: Meiss al-Jabal

In particular, in the village of Meiss al-Jabal, in South Lebanon, born from a collaboration with Staffan de Mistura, High Representative of the UN Secretary General in the region, as a support action for the refugee communities, it was provided with two digitized classrooms, satellite connection and various specialized devices for remote consultation and assistance services, obtaining a rapid professionalization of the students, to offer them hope for the future. Unfortunately, with the War in Lebanon in 2006, many villages have been destroyed, including Meiss al-Jabal. Moreover, OCCAM promoted the birth of the Beirut Film Festival with the Ministry of Culture and the International Council for Film Television and Audiovisual Communication, and the reconstruction of the National Film Archive to make a contribution to the UN Peacekeeping action.

John Shirley, at that time, President of the Navajo Nation, at the WSIS - Tunis, 2005, where he announced the birth of the Navajo Nation Portal.

ICT Village and Navajo Nation

John Shirley, at that time, President of the Navajo Nation, at the World Summit on the Information Society, organized by the ITU in Tunis, 2005, where he announced the birth of the Navajo Nation Portal. Another important project is the Navajo Nation Portal, announced in 2005 during the intervention at the WSIS in Tunis by John Shirley, president of the Navajo Nation co-signatory of the Memorandum of understanding with ITU and OCCAM, for the development of digitalization in indigenous populations, which sees the creation in many pueblos of access and training centers.

ICT Village in Madagascar: The UN Millennium Village of Sambaina

A longlasting project is the ICT Village of Sambaina, born also thanks to the support of the then President of the Malgasy Republic S.E. Marc Ravalomanana.

Here the project has been developed focusing on:

2006, Jeffrey Sachs with Pierpaolo Saporito, president of OCCAM and the Permanent Representative of Madagascar to the United Nations, Zina Andrianarivelo at the Sambaina ICT Village. In this occasion Sambaina was proclaimed Millennium Village by Jeffrey Sachs.

• telemedicine, with the establishment of a new digitalized health unit, especially on maternal care, achieving a reduction in pre-postpartum and early childhood mortality,

• e-learning, with classes equipped with computers and other digital devices and courses.

• center for internet access for the population of the district.

All the vast territory, after a first satellite coverage provided by Eutelsat / Skylogic, was connected in broadband using the state frequencies, so that hospitals, schools, municipalities, operated without charges, stating the principle, then decided in UNGAID, that public services must be able to take advantage of public broadband networks.

Sambaina soon arouses international attention, including the visit of Jeffrey Sachs, director of the UN Millennium Project and Special Advisor of the Secretary General Kofi Annan, who proclaimed him in 2006 the first and only one of its kind, Millennium Village towards which both UNDP and the Millennium Challenge Corporation USA will launch support programs.

The Ville Village Project

In support of Sambaina and the other ICT Village, OCCAM launched the Ville Village Project in 2005 to encourage direct collaboration  between communities in developing countries and cities in advanced countries, which have greatly encouraged the integration in the perspective of mutual cultural and social enrichment and in order to optimize the resources put in place by both local authorities and NGOs in development cooperation projects.

The first Ville-Village realization was ratified with the agreement signed by the Ambassador of Madagascar in Italy, H.E. Jean Pierre Razafi, on 4 December 2008, and the mayor of Lodi Lorenzo Guerini, Within this initiative the city of Lodi has been selected to better employ the features of its territory, such as the Padano Technology Park, the Hospital (already active in the telemedicine sector) and the NGOs operating in its territory. Innovative digital development service centers have also been created, focusing on e-phytopathology, and e-veterinary.

The ICT Village in Lesotho: Mahobong

The ICT Village of Mahobong, in Lesotho, experimented in 2007 the Digital Services Global Platform, both in the field of Food Security with applications of e-phytopathology and parasitology and of telemedicine, through a new ultrasound device, which allow remote ultrasounds suitable for prevent pre- and post-natal mortality and assist emergency interventions. The project realized by OCCAM in collaboration with the Department of Protection of Agrifood and Urban Systems and Biodiversity Valorization of the University of Milan and with the International Telemedicine Institute (IITM), supported by the Municipality of Milan, has allowed to export knowledge in the field of cultivation and protection of plants and food and limit production losses caused to production, giving considerable development to the communities involved.

Smart villages in Asia

According to a publication written for the International Finance Corporation (IFC) in 2012, Asia has the largest off-grid population in the world, with 55% of the global off-grid population, and 798 million people having no access to electricity. As per estimates about 700 million or 90% were located in rural Asia. However, research studies reveal that South Asian and Sub-Saharan African countries have been unable to expand their electrification rate. Whereas electrification progress in regions such as Latin America and East Asia (China) indicates a rapid growth. Central Asian countries are blessed with sufficient resources and export their extra electricity to neighboring countries.

Electrification is highly desired by all rural communities. Different international, national and local organizations use different indicators for measuring and reporting mini-grids or stand-alone systems. South Asian countries have been focusing on off-grid electrification of current trend for Rural Electrification (RE) at regional level. India, Bangladesh, Sri Lanka and Nepal have shown good results for RE through off-grid communities.

Eastern Asia/East Asia

About 38% of the population of Asia and 22% in the world, live in East Asia.

 Japan Public confidence in safety of nuclear power was greatly damaged by the Fukushima Daiichi nuclear disaster, consequently Off-grid concept was applied more conveniently in Japan. Alternative energy technologies have become standard in newly constructed homes. Sekisui House Ltd, a famous Japanese house building firm pointed out that 80% of single-family homes were constructed with alternative energy technology such as solar panels and fuel cells. Reflecting the nation's mood, Executive Director of Sekisui Company states that "If you’re going to use electricity, you might as well make it yourself".

 South Korea In June, 2015 Smart Villages (New thinking for off-grid communities worldwide) has conducted a workshop in Seoul to familiarize the people of South Korea about the fast evolving technology moving towards off-grid communities and its support for bright ideas and entrepreneurial efforts in the field of rural energy access.

Wind farm in Xinjiang, China

 China According to IRENA report China in 2013, besides wind farms, had roughly 60,000 diesel and hydro mini-grid systems, most of them connected to the centralized grid. It has further installed 118 GW of solar Photovoltaics systems, of which 500 MW was installed in off-grid systems.

 Malaysia Acknowledging Solar as green technology, Malaysia has been encouraging solar power for rural electrification and reaffirmed its support of research into off-grid electricity through alternate energy. Malaysian Government also considering potential of smart villages and each village would differ according to the needs of its population.

 Indonesia People of Indonesia living in rural areas have been facing low electrification and using fossil fuel for power supply. Additionally many remote communities still lack access to any power at all with little expectation of being supplied on-grid power by the state-owned electricity company (PLN). In the recent past Indonesian government has initiated a properly developed, constructed and sustainable community-owned renewable energy plan to raise the quality of life in rural communities, and under this project plants should be owned, managed and maintained by the rural communities. In 2013 EnDev Indonesia was awarded first prize in the category “Community-based Off-grid”, with its project on micro-hydro power in Lembah Derita, Sumatra Barat.

 Philippines With more than 2,000 inhabited islands, it is difficult in Philippines to extend electrical grid to communities in remote areas. In a documentary presentation, CEO of Hybrid Social Solutions Inc. indicated distribution of solar products that have been delivered to poor communities across the Philippines with a future plan of building an ecosystem to support standalone solar energy devices for use by the rural communities. They have also considered it essential to ensure the sustainability and future growth in remote areas with community based solar projects.

 North Korea has been focusing on modern technologies for overcoming its chronic energy shortage. Utilization of alternative energy sources in place of fossil fuel consumption is being considered to satisfy the socio-economic requirements of its people.

Western Asia/West Asia and Middle East

Geographical marking in the Western Asia consists of 19 countries/territorial states. 5 countries of Asia from this region hold strong financial stability and resources for social development. In this region three countries, According to population demography Turkey, Iraq and Yemen stand at 10th, 13th and 20th position respectively.

 Turkey With a substantial potential for the renewable energy resources, Turkey holds seventh position in the world (and first in Europe) in terms of geothermal energy. It has also planned to further increase its hydro, wind and solar energy resources. Turkey envisages producing 30% of its electricity need from the renewable by 2023.

 Iraq Ten years after the war, the power supply was short of demand. But in April, 2013 Oil Ministry of Iraq highlighted its plan stating that: "By the end of 2013, the crisis will be over for households with supply of electricity around the clock across the country. By the end of 2014, Iraq would have met industrial demand as well”. However, political instability and role of terrorism by the terrorists in Iraq reliable and neutral assessment is still a hard job.

 Yemen Prior to Saudi Arabian-led intervention in Yemen in Yemen, energy and power supply scenario reveals that 93% Yemenis rural population was using gas canisters as their primary source of fuel. They also spent 55% of their income on food, water and energy. Power supply, where available, comes from government-run plants, the majority of which run on diesel. New capacity additions were slow with poor transmission network

Northern Asia

 Russia Covers largest part of Asia with a 17,098,242 km2 area in the Northern sub-region of Asia. Russia is the world's fourth largest electricity producer after the United States, China, and Japan. Russia exports electricity to countries e.g. Latvia, Lithuania, Poland etc. However, import and export reversal has also been reported due to cost of production.

South Asia

 Afghanistan With its insufficient power supply infrastructure covers its electricity demand through import from electricity-exporting countries i.e. Uzbekistan, Tajikistan, Turkmenistan and Iran, these countries mostly sell their surplus electricity to Afghanistan. Above 4 billion US dollars have so far been disbursed to build power supply infrastructure in Afghanistan but deficiencies not only to its rural/remotes areas but country's capital needs more considerable help from developed countries for supply of electrification to whole Afghanistan One of the largest solar power project funded at a cost $18 by the government of New Zealand has started functioning for supply of energy to 2,500 households, businesses and government buildings in central Bamyan Province of Afghanistan.

 Bangladesh According to a World Bank document, about 62% of Bangladesh's population had access to electricity in 2013, indicating 90% and 43% wide disparity between urban and rural areas. Bangladesh while standing at 134th out of 144 countries on the quality of electricity supply, Renewable Energy for Rural Economic Development (RERED) Project sought to raise levels of social development and economic growth by increasing access to electricity in rural areas. Under REFED notable contribution to social and economic outcomes in rural areas by extending access to electricity through off-grid Solar Home Systems (SHS), has been witnessed and noted with significant increase in Household appliances. The World Bank report envisaged that Off-grid systems can accelerate the benefits of “lighting” in a cost-effective manner, to populations that face uncertain waiting periods for grid-based electricity, or are unlikely to obtain grid-based electricity due to remote or inaccessible locations. Report also focus the role of off-grid communities based on public-private partnership model for off-grid electricity services to the deprived population of Bangladesh.

 India With mini-grids and off-grid applications, India is a leading country. The Jawaharlal Nehru National Solar Mission (JNNSM) is its main policy initiative to promote solar energy, including off-grid power development. International Finance Corporation (IFC) and the World Bank collaborate with various stakeholders for global off-grid lighting market for reliable electricity to people who have no access to national grids. A neutral, independent, not for profit association called Global Off-Grid Lighting Association (GOGLA) was conceived out of a joint World Bank/IFC Lighting Africa and private sector effort in 2012. India is the first Asian lighting programs for IFC. Lighting Asia/India program was planned to enable access of two million rural Indians to off-grid lighting solutions by 2015. The program is designed with a series of interventions to alter market behavior by removing specific barriers, for example, the market spoilage created by poor products, lack of information on quality products and on distribution channels, lack of financing for companies and consumers, lack of awareness that quality solar appliances are affordable and viable.

Solar cells

India's first smart village has been developed by Eco Needs Foundation at Dhanora village of Rajasthan. The concept is prepared by Prof. Priyanand Agale, Dr. Satyapal Singh Meena an officer of Indian Revenue Service (IRS) also copyrights of smart village is on the name of Prof.Priyanand Agale ,Dr.Satyapal Singh Meena and Mr. Attdeep Agale. This concept consists of five elements Retrofitting, Redevelopment, Greenfield, e-Pan and Livelihood. Under the project of smart village the Foundation is adopting villages and putting efforts for sustainable development by providing basic amenities like sanitation, safe drinking water, internal road, tree plantation, water conservation. The Foundation is also working for inculcating moral values in the society and for improving the standard of living of the villagers. The Foundation has developed Village Dhanora, Teh. Bari, District Dholpur, one of the remotely situated village of Rajasthan as India's First Smart Village. The village is situated 30 km away from Dholpur district headquarter and 248 km from Jaipur city, Capital of Rajasthan State of India. The population of the village was nearly about 2000 having no sanitation facility, potable water facility, which were adversely causing the health of the villagers. The internal roads are also not there and it causes great hardship to the people especially in rainy season. Owing to unawareness and non-availability of sanitation facility and toilets the people of the village use to go open for defecation. There are other problems also which villagers were facing such as Fluoride concentration in drinking water, No water conservation System, Encroachment on the roads, Electrical power fluctuation No outcome base education, Unemployment and poverty. ProF. Priyanand Agale Founder, president of Foundation and Dr. Satyapal Singh Meena officer of the Indian Revenue service has converted this village as India's first Smart Village and now Dhanora has become a role model of Rural Development. Following are the major success achieved within a short span of two years of the project and project is still underway:

  1. Construction of 822 toilets in the Panchayat area with the help of district administration and public participation accordingly, the Dhanora Gram Panchayat has been declared as the first “Open Defecation Free” (ODF) Panchayat by District Administration.
  2. Village Dhanora become India's first village having sewerage line with treatment plant. The Foundation has laid down nearly 2 km long sewerage line of diameter 450 mm in the village. Each of the toilets of Dhanora village have been connected to sewerage line with inspection chambers.
  3. Construction of nearly 2 km long cement concrete internal roads constructed with 3.5 m to 4.5 m width with high quality.
  4. Construction of eight Percolation tank connecting with nearly 2.5 km artificial channel of 10 feet in width and 10 feet in depth for water conservation and ground water recharge with public participation and with the help of government having groundwater recharge capacity of 97.49 Million liters in one time recharge, which will provide irrigation facility to farms of the village and nearby villages resulting into economic growth of farmers.
  5. The work of the removal of encroachments and road widening has been completed without using any police force. Now the whole village is having motorable road in the village.
  6. Construction of nearly 2 km approach road at Dhodekapura village of the Dhanora Panchayat, which was not done in last 65 years.
  7. The police Administration is going to declare the village as “APRADH MUKKTA GAON” (Crime Free Village), no case or FIR in Police Station.
  8. village Dhanora has been converted into an Art gallery. The paintings in the village are spreading social awareness among villagers
  9. The foundation stone for community centre and information centre has been laid down, work under progress. Work of solar street light, skill development centre, library, meditation centre, sport complex, Wi-Fi facility, and community toilet will be taken up in due course of time and as per availability of funds.

 Maldives President of Maldives has already launched an initiative to make the Maldives a solar power stronghold to provide rooftop solar panels in the rural and remote areas of the country. Under this project together with a plan to achieve carbon neutral Maldives by 2020, first solar energy panels was installed in one of the school in Villimale district of Male that accompanied the inauguration of the project. However, Maldives needs more concentration over electrification demand of its people especially in rural and remote areas.

   Nepal Hydro power and solar resources are sufficient enough to satisfy the electricity demand of the Nepal. However, most of the country's current energy needs are met with inefficiently used biomass, including firewood (75%), agricultural residues (4%) and animal waste (6%). The rest is met by commercial sources, including petroleum, coal and electricity. Only about 12 percent of the country's population uses electricity derived from water, wind or sun.In Nepal above 50% households mostly in urban or semi-urban areas are connected to the national grid. Its 80% population is rural. Government of Nepal has launched National Rural and Renewable Energy Program in 2012 with subsidize strategy in an attempt to electrify long-deprived areas. Per Nepal Living Standard Survey 2011 estimates 96% urban 63% rural population has access to electricity. With a total capacity of 107 kW, Nepal's first mini-grid of its kind was set up in 2012 connecting the micro-hydro plants in Rangkhani, Paiyuthanthap, Sarkuwa and Damek. Besides UNDP is encouraging to put the community at the center of the planning, installing, and operating processes of micro-hydro plants.

Jhimpir Wind Farm

 Pakistan Geographically is located to a place where exploitation of solar energy is most conducive, as it is 6th country in the world in terms of solar irradiance where sunshine availability is 8 to 10 hours per day in its most parts. Mini wind farming projects (1-50 kWatts) along with small solar farms scattered over remote inaccessible areas. Use of solar energy in rural villages of Pakistan with solar panels is growing on off-grid concept with increasing community systems. The Aga Khan Rural Support Programme and the Sarhad Rural Support Programme (through Programme for Economic Advancement and Community Empowerment) have been encouraging village organizations to promote and establish community based micro hydro power projects across Khyber Pakhtunkhwa, Gilgit–Baltistan, Federally Administered Tribal Areas and Azad Jammu and Kashmir districts and villages. Both Rural Support Programmes have received the Ashden Award in this regard. The Khyber Pakhtunkhwa Government has also decided to increase the number of micro hydropower generation projects to 1000, with total power generation capacity of 100 megawatts (MW). There are several barriers that are keepng Pakistan from nationwide off grid electrification, in spite of enough wind, water and sun to poweroff grid communities in Pakistan but the rate of conversion from no energy to alternative energy remains slow. As per World Bank Study, almost 44% of Pakistani households have no access to grid based electricity. 80% of this deprived population resides in remote and rural areas.

900MW Lakvijaya Power Station

 Sri Lanka Off-grid electrification schemes are still operating in Sri Lanka in spite 89% systematic grid expansion projects carried out by the Sri Lankan government to national grid. Most of the Village Hydro Schemes (VHS) in Sri Lanka are aided by RERED project funded by World Bank and Global Environment Facility (GEF) these initiatives have established 100-150 Village Hydro Schemes in the country with capacities ranging from 3-50 kW. However, off-grid generation is a diminishing component on the supply side. This is a result of the fast expanding national grid, which now serves more than 90% of all homes.

Awareness competitions in India and Pakistan

Access to reliable and uninterrupted electricity is a chronic demand in villages all over the world. The best solution for overcoming this problem is utilization of alternative energy with modern advancement with implementation of off-grid system.

India

In India competition[73] for all enthusiastic entrepreneurs, individuals and organizations running energy access programmes and businesses in rural villages in India has also been launched and is about to close in November-2015. The participants were asked to highlight close sustainable examples where off-grid system is being practiced providing a platform for "energy entrepreneurs" to discuss the ways for achieving off-grid systems. This competition has also good rewards for successful winners i.e. Cash Prize of I million Indian Rupees, a trip to world Sustainable Development Forum to showcase their business on the main stage, etc.

Pakistan

Pie chart of survey conducted by a female student of Aga Khan Higher Secondary School.

In Pakistan the Agha Khan University Examination Board in October-2015 launched a "Poster Competition" with the title "when ideas flow villages grow" as an initiative to introduce the idea of Smart Villages among young students and to evaluate best measures for its implementation.The most outstanding poster presentation from across the country will get a chance to visit the University of Cambridge, UK, besides other good prizes.

A female student of Aga Khan Higher Secondary School, one of the participant of the competition conducted the survey from her home place to villagers in remote areas by making connections with them through social media and cellular phones. According to her survey 50% of the people were found not satisfied with the rural electrification rate of PEPCO and other power distribution companies. They also believe that off grid system is now a need for the villages of Pakistan. Majority of the people were in favor of installing solar panels and wind turbines for energy generation in remote areas of Pakistan to boost up the development in energy sector of the country.

Smart villages in Europe

The concept of smart villages has been discussed in the European context, for example with regards to some communities in Czech Republic, Montenegro and Poland. For example, some offshoots of the European Youth Parliament are going to debate the topic as a part of a debate on regional development.

Analytics

From Wikipedia, the free encyclopedia

Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance.

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics. According to International Data Corporation, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021. As per Gartner, the overall analytic platforms software market grew by $25.5 billion in 2020.

Analytics vs analysis

Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data. Data analytics is used to formulate larger organizational decisions.

Data analytics is a multidisciplinary field. There is extensive use of computer skills, mathematics, statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data through analytics. There is increasing use of the term advanced analytics, typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks, decision trees, logistic regression, linear to multiple regression analysis, and classification to do predictive modeling. It also includes unsupervised machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis and association analysis.

Applications

Marketing optimization

Marketing organizations use analytics to determine the outcomes of campaigns or efforts, and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

Marketing analytics consists of both qualitative and quantitative, structured and unstructured data used to drive strategic decisions about brand and revenue outcomes. The process involves predictive modelling, marketing experimentation, automation and real-time sales communications. The data enables companies to make predictions and alter strategic execution to maximize performance results.

Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify the IP address, and track the activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.

Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics e.g.: segmentation. Web analytics and optimization of websites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing mix modeling context.

These tools and techniques support both strategic marketing decisions (such as how much overall to spend on marketing, how to allocate budgets across a portfolio of brands and the marketing mix) and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost-effective medium at the ideal time.

People analytics

People analytics uses behavioral data to understand how people work and change how companies are managed.

People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. HR analytics is the application of analytics to help companies manage human resources. Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. For example, inspection of the strategic phenomenon of employee turnover utilizing People Analytics Tools may serve as an important analysis at times of disruption.  It has been suggested that People Analytics is a separate discipline to HR analytics, representing a greater focus on business issues rather than administrative processes, and that People Analytics may not really belong within Human Resources in organizations. However, experts disagree on this, with many arguing that Human Resources will need to develop People Analytics as a key part of a more capable and strategic business function in the changing world of work brought on by automation. Instead of moving People Analytics outside HR, some experts argue that it belongs in HR, albeit enabled by a new breed of HR professional who is more data-driven and business savvy.

Portfolio analytics

A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status (wealthy, middle-class, poor, etc.) of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.

The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand, there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.

Risk analytics

Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict an individual's delinquency behavior and are widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like online payment gateway companies to analyse if a transaction was genuine or fraud. For this purpose, they use the transaction history of the customer. This is more commonly used in Credit Card purchases, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him/her. This helps in reducing loss due to such circumstances.

Digital analytics

Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automation. This also includes the SEO (search engine optimization) where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics. A growing number of brands and marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is an important key performance indicator (KPI).

Security analytics

Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest risk. Products in this area include security information and event management and user behavior analytics.

Software analytics

Software analytics is the process of collecting information about the way a piece of software is used and produced.

Challenges

In the industry of commercial analytics software, an emphasis has emerged on solving the challenges of analyzing massive, complex data sets, often when such data is in a constant state of change. Such data sets are commonly referred to as big data. Whereas once the problems posed by big data were only found in the scientific community, today big data is a problem for many businesses that operate transactional systems online and, as a result, amass large volumes of data quickly.

The analysis of unstructured data types is another challenge getting attention in the industry. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant effort at data transformation. Sources of unstructured data, such as email, the contents of word processor documents, PDFs, geospatial data, etc., are rapidly becoming a relevant source of business intelligence for businesses, governments and universities. For example, in Britain the discovery that one company was illegally selling fraudulent doctor's notes in order to assist people in defrauding employers and insurance companies is an opportunity for insurance firms to increase the vigilance of their unstructured data analysis.

These challenges are the current inspiration for much of the innovation in modern analytics information systems, giving birth to relatively new machine analysis concepts such as complex event processing, full text search and analysis, and even new ideas in presentation. One such innovation is the introduction of grid-like architecture in machine analysis, allowing increases in the speed of massively parallel processing by distributing the workload to many computers all with equal access to the complete data set.

Analytics is increasingly used in education, particularly at the district and government office levels. However, the complexity of student performance measures presents challenges when educators try to understand and use analytics to discern patterns in student performance, predict graduation likelihood, improve chances of student success, etc. For example, in a study involving districts known for strong data use, 48% of teachers had difficulty posing questions prompted by data, 36% did not comprehend given data, and 52% incorrectly interpreted data. To combat this, some analytics tools for educators adhere to an over-the-counter data format (embedding labels, supplemental documentation, and a help system, and making key package/display and content decisions) to improve educators' understanding and use of the analytics being displayed.

Risks

Risks for the general population include discrimination on the basis of characteristics such as gender, skin colour, ethnic origin or political opinions, through mechanisms such as price discrimination or statistical discrimination.

Learning analytics

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Learning_analytics

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. The growth of online learning since the 1990s, particularly in higher education, has contributed to the advancement of Learning Analytics as student data can be captured and made available for analysis. When learners use an LMS, social media, or similar online tools, their clicks, navigation patterns, time on task, social networks, information flow, and concept development through discussions can be tracked. The rapid development of massive open online courses (MOOCs) offers additional data for researchers to evaluate teaching and learning in online environments.

Definition

Although a majority of Learning Analytics literature has started to adopt the aforementioned definition, the definition and aims of Learning Analytics are still contested.

George Siemens is a writer, theorist, speaker, and researcher on learning, networks, technology, analytics and visualization, openness, and organizational effectiveness in digital environments. He is the originator of Connectivism theory and author of the article Connectivism: A Learning Theory for the Digital Age and the book Knowing Knowledge – an exploration of the impact of the changed context and characteristics of knowledge. He is the founding President of the Society for Learning Analytics Research (SoLAR).

Learning Analytics as a prediction model

One earlier definition discussed by the community suggested that Learning Analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning. But this definition has been criticised by George Siemens and Mike Sharkey.


Learning Analytics as a generic design framework

Dr. Wolfgang Greller and Dr. Hendrik Drachsler defined learning analytics holistically as a framework. They proposed that it is a generic design framework that can act as a useful guide for setting up analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. It uses a general morphological analysis (GMA) to divide the domain into six "critical dimensions".

Learning Analytics as data-driven decision making

The broader term "Analytics" has been defined as the science of examining data to draw conclusions and, when used in decision-making, to present paths or courses of action. From this perspective, Learning Analytics has been defined as a particular case of Analytics, in which decision-making aims to improve learning and education. During the 2010s, this definition of analytics has gone further to incorporate elements of operations research such as decision trees and strategy maps to establish predictive models and to determine probabilities for certain courses of action.

Learning Analytics as an application of analytics

Another approach for defining Learning Analytics is based on the concept of Analytics interpreted as the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data. From this point of view, Learning Analytics emerges as a type of Analytics (as a process), in which the data, the problem definition and the insights are learning-related.

In 2016, a research jointly conducted by the New Media Consortium (NMC) and the EDUCAUSE Learning Initiative (ELI) -an EDUCAUSE Program- describes six areas of emerging technology that will have had significant impact on higher education and creative expression by the end of 2020. As a result of this research, Learning analytics was defined as an educational application of web analytics aimed at learner profiling, a process of gathering and analyzing details of individual student interactions in online learning activities.

Dragan Gašević is a pioneer and leading researcher in learning analytics. He is a founder and past President (2015-2017) of the Society for Learning Analytics Research (SoLAR).

Learning analytics as an application of data science

In 2017, Gašević, Коvanović, and Joksimović proposed a consolidated model of learning analytics. The model posits that learning analytics is defined at the intersection of three disciplines: data science, theory, and design. Data science offers computational methods and techniques for data collection, pre-processing, analysis, and presentation. Theory is typically drawn from the literature in the learning sciences, education, psychology, sociology, and philosophy. The design dimension of the model includes: learning design, interaction design, and study design. In 2015, Gašević, Dawson, and Siemens argued that computational aspects of learning analytics need to be linked with the existing educational research in order for Learning Analytics to deliver its promise to understand and optimize learning.

Learning analytics versus educational data mining

Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that "...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior". They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks questions whether this distinction exists in the literature. Brooks instead proposes that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems (e.g. learning content management systems).

Regardless of the differences between the LA and EDM communities, the two areas have significant overlap both in the objectives of investigators as well as in the methods and techniques that are used in the investigation. In the MS program offering in learning analytics at Teachers College, Columbia University, students are taught both EDM and LA methods.

Historical contributions

Learning Analytics, as a field, has multiple disciplinary roots. While the fields of artificial intelligence (AI), statistical analysis, machine learning, and business intelligence offer an additional narrative, the main historical roots of analytics are the ones directly related to human interaction and the education system. More in particular, the history of Learning Analytics is tightly linked to the development of four Social Sciences' fields that have converged throughout time. These fields pursued, and still do, four goals:

  1. Definition of Learner, in order to cover the need of defining and understanding a learner.
  2. Knowledge trace, addressing how to trace or map the knowledge that occurs during the learning process.
  3. Learning efficiency and personalization, which refers to how to make learning more efficient and personal by means of technology.
  4. Learner – content comparison, in order to improve learning by comparing the learner's level of knowledge with the actual content that needs to master.[5](Siemens, George (2013-03-17). Intro to Learning Analytics. LAK13 open online course for University of Texas at Austin & Edx. 11 minutes in. Retrieved 2018-11-01.)

A diversity of disciplines and research activities have influenced in these 4 aspects throughout the last decades, contributing to the gradual development of learning analytics. Some of most determinant disciplines are Social Network Analysis, User Modelling, Cognitive modelling, Data Mining and E-Learning. The history of Learning Analytics can be understood by the rise and development of these fields.

Social Network Analysis

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Social network analysis is prominent in Sociology, and its development has had a key role in the emergence of Learning Analytics. One of the first examples or attempts to provide a deeper understanding of interactions is by Austrian-American Sociologist Paul Lazarsfeld. In 1944, Lazarsfeld made the statement of "who talks to whom about what and to what effect". That statement forms what today is still the area of interest or the target within social network analysis, which tries to understand how people are connected and what insights can be derived as a result of their interactions, a core idea of Learning Analytics.

Citation analysis

American linguist Eugene Garfield was an early pioneer in analytics in science. In 1955, Garfield led the first attempt to analyse the structure of science regarding how developments in science can be better understood by tracking the associations (citations) between articles (how they reference one another, the importance of the resources that they include, citation frequency, etc). Through tracking citations, scientists can observe how research is disseminated and validated. This was the basic idea of what eventually became a "page rank", which in the early days of Google (beginning of the 21st century) was one of the key ways of understanding the structure of a field by looking at page connections and the importance of those connections. The algorithm PageRank -the first search algorithm used by Google- was based on this principle. American computer scientist Larry Page, Google's co-founder, defined PageRank as "an approximation of the importance" of a particular resource. Educationally, citation or link analysis is important for mapping knowledge domains.

The essential idea behind these attempts is the realization that, as data increases, individuals, researchers or business analysts need to understand how to track the underlying patterns behind the data and how to gain insight from them. And this is also a core idea in Learning Analytics.

Digitalization of Social network analysis

During the early 1970s, pushed by the rapid evolution in technology, Social network analysis transitioned into analysis of networks in digital settings.

  1. Milgram's 6 degrees experiment. In 1967, American social psychologist Stanley Milgram and other researchers examined the average path length for social networks of people in the United States, suggesting that human society is a small-world-type network characterized by short path-lengths.
  2. Weak ties. American Sociologist Mark Granovetter's work on the strength of what is known as weak ties; his 1973 article "The Strength of Weak Ties" is one of the most influential and most cited articles in Social Sciences.
  3. Networked individualism. Towards the end of the 20th century, Sociologist Barry Wellman's research extensively contributed the theory of social network analysis. In particular, Wellman observed and described the rise of "networked individualism" – the transformation from group-based networks to individualized networks.


During the first decade of the century, Professor Caroline Haythornthwaite explored the impact of media type on the development of social ties, observing that human interactions can be analyzed to gain novel insight not from strong interactions (i.e. people that are strongly related to the subject) but, rather, from weak ties. This provides Learning Analytics with a central idea: apparently un-related data may hide crucial information. As an example of this phenomenon, an individual looking for a job will have a better chance of finding new information through weak connections rather than strong ones. (Siemens, George (2013-03-17). Intro to Learning Analytics. LAK13 open online course for University of Texas at Austin & Edx. 11 minutes in. Retrieved 2018-11-01.)

Her research also focused on the way that different types of media can impact the formation of networks. Her work highly contributed to the development of social network analysis as a field. Important ideas were inherited by Learning Analytics, such that a range of metrics and approaches can define the importance of a particular node, the value of information exchange, the way that clusters are connected to one another, structural gaps that might exist within those networks, etc.

The application of social network analysis in digital learning settings has been pioneered by Professor Shane P. Dawson. He has developed a number of software tools, such as Social Networks Adapting Pedagogical Practice (SNAPP) for evaluating the networks that form in [learning management systems] when students engage in forum discussions.

User modelling

The main goal of user modelling is the customization and adaptation of systems to the user's specific needs, especially in their interaction with computing systems. The importance of computers being able to respond individually to into people was starting to be understood in the decade of 1970s. Dr Elaine Rich in 1979 predicted that "computers are going to treat their users as individuals with distinct personalities, goals, and so forth". This is a central idea not only educationally but also in general web use activity, in which personalization is an important goal.

User modelling has become important in research in human-computer interactions as it helps researchers to design better systems by understanding how users interact with software. Recognizing unique traits, goals, and motivations of individuals remains an important activity in learning analytics.

Personalization and adaptation of learning content is an important present and future direction of learning sciences, and its history within education has contributed to the development of learning analytics.Hypermedia is a nonlinear medium of information that includes graphics, audio, video, plain text and hyperlinks. The term was first used in a 1965 article written by American Sociologist Ted Nelson. Adaptive hypermedia builds on user modelling by increasing personalization of content and interaction. In particular, adaptive hypermedia systems build a model of the goals, preferences and knowledge of each user, in order to adapt to the needs of that user. From the end of the 20th century onwards, the field grew rapidly, mainly due to that the internet boosted research into adaptivity and, secondly, the accumulation and consolidation of research experience in the field. In turn, Learning Analytics has been influenced by this strong development.

Education/cognitive modelling

Education/cognitive modelling has been applied to tracing how learners develop knowledge. Since the end of the 1980s and early 1990s, computers have been used in education as learning tools for decades. In 1989, Hugh Burns argued for the adoption and development of intelligent tutor systems that ultimately would pass three levels of "intelligence": domain knowledge, learner knowledge evaluation, and pedagogical intervention. During the 21st century, these three levels have remained relevant for researchers and educators.

In the decade of 1990s, the academic activity around cognitive models focused on attempting to develop systems that possess a computational model capable of solving the problems that are given to students in the ways students are expected to solve the problems. Cognitive modelling has contributed to the rise in popularity of intelligent or cognitive tutors. Once cognitive processes can be modelled, software (tutors) can be developed to support learners in the learning process. The research base on this field became, eventually, significantly relevant for learning analytics during the 21st centur

Epistemic Frame Theory

While big data analytics has been more and more widely applied in education, Wise and Shaffer addressed the importance of theory-based approach in the analysis. Epistemic Frame Theory conceptualized the "ways of thinking, acting, and being in the world" in a collaborative learning environment. Specifically, the framework is based on the context of Community of Practice (CoP), which is a group of learners, with common goals, standards and prior knowledge and skills, to solve a complex problem. Due to the essence of CoP, it is important to study the connections between elements (learners, knowledge, concepts, skills and so on). To identify the connections, the co-occurrences of elements in learners' data are identified and analyzed.

Shaffer and Ruis pointed out the concept of closing the interpretive loop, by emphasizing the transparency and validation of model, interpretation and the original data. The loop can be closed by a good theoretical sound analytics approaches, Epistemic Network Analysis.

Other contributions

In a discussion of the history of analytics, Adam Cooper highlights a number of communities from which learning analytics has drawn techniques, mainly during the first decades of the 21st century, including:

  1. Statistics, which are a well established means to address hypothesis testing.
  2. Business intelligence, which has similarities with learning analytics, although it has historically been targeted at making the production of reports more efficient through enabling data access and summarising performance indicators.
  3. Web analytics, tools such as Google Analytics report on web page visits and references to websites, brands and other key terms across the internet. The more "fine grain" of these techniques can be adopted in learning analytics for the exploration of student trajectories through learning resources (courses, materials, etc.).
  4. Operational research, which aims at highlighting design optimisation for maximising objectives through the use of mathematical models and statistical methods. Such techniques are implicated in learning analytics which seek to create models of real world behaviour for practical application.
  5. Artificial intelligence methods (combined with machine learning techniques built on data mining) are capable of detecting patterns in data. In learning analytics such techniques can be used for intelligent tutoring systems, classification of students in more dynamic ways than simple demographic factors, and resources such as "suggested course" systems modelled on collaborative filtering techniques.
  6. Information visualization, which is an important step in many analytics for sensemaking around the data provided, and is used across most techniques (including those above).

Learning analytics programs

The first graduate program focused specifically on learning analytics was created by Ryan S. Baker and launched in the Fall 2015 semester at Teachers College, Columbia University. The program description states that

"(...)data about learning and learners are being generated today on an unprecedented scale. The fields of learning analytics (LA) and educational data mining (EDM) have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators. As one of world's leading teaching and research institutions in education, psychology, and health, we are proud to offer an innovative graduate curriculum dedicated to improving education through technology and data analysis."

Masters programs are now offered at several other universities as well, including the University of Texas at Arlington, the University of Wisconsin, and the University of Pennsylvania.

Analytic methods

Methods for learning analytics include:

  • Content analysis, particularly of resources which students create (such as essays).
  • Discourse analytics, which aims to capture meaningful data on student interactions which (unlike social network analytics) aims to explore the properties of the language used, as opposed to just the network of interactions, or forum-post counts, etc.
  • Social learning analytics, which is aimed at exploring the role of social interaction in learning, the importance of learning networks, discourse used to sensemake, etc.
  • Disposition analytics, which seeks to capture data regarding student's dispositions to their own learning, and the relationship of these to their learning. For example, "curious" learners may be more inclined to ask questions, and this data can be captured and analysed for learning analytics.
  • Epistemic Network Analysis, which is an analytics technique that models the co-occurrence of different concepts and elements in the learning process. For example, the online discourse data can be segmented as turn of talk. By coding students' different behaviors of collaborative learning, we could apply ENA to identify and quantify the co-occurrence of different behaviors for any individual in the group.

Applications

Learning Applications can be and has been applied in a noticeable number of contexts.

General purposes

Analytics have been used for:

  • Prediction purposes, for example to identify "at risk" students in terms of drop out or course failure.
  • Personalization & adaptation, to provide students with tailored learning pathways, or assessment materials.
  • Intervention purposes, providing educators with information to intervene to support students.
  • Information visualization, typically in the form of so-called learning dashboards which provide overview learning data through data visualisation tools.

Benefits for stakeholders

There is a broad awareness of analytics across educational institutions for various stakeholders, but that the way learning analytics is defined and implemented may vary, including:

  1. for individual learners to reflect on their achievements and patterns of behaviour in relation to others. Particularly, the following areas can be set out for measuring, monitoring, analyzing and changing to optimize student performance:
    1. Monitoring individual student performance
    2. Disaggregating student performance by selected characteristics such as major, year of study, ethnicity, etc.
    3. Identifying outliers for early intervention
    4. Predicting potential so that all students achieve optimally
    5. Preventing attrition from a course or program
    6. Identifying and developing effective instructional techniques
    7. Analyzing standard assessment techniques and instruments (i.e. departmental and licensing exams)
    8. Testing and evaluation of curricula.
  2. as predictors of students requiring extra support and attention;
  3. to help teachers and support staff plan supporting interventions with individuals and groups;
  4. for functional groups such as course teams seeking to improve current courses or develop new curriculum offerings; and
  5. for institutional administrators taking decisions on matters such as marketing and recruitment or efficiency and effectiveness measures.

Some motivations and implementations of analytics may come into conflict with others, for example highlighting potential conflict between analytics for individual learners and organisational stakeholders.

Software

Much of the software that is currently used for learning analytics duplicates functionality of web analytics software, but applies it to learner interactions with content. Social network analysis tools are commonly used to map social connections and discussions. Some examples of learning analytics software tools include:

  • BEESTAR INSIGHT: a real-time system that automatically collects student engagement and attendance, and provides analytics tools and dashboards for students, teachers and management
  • LOCO-Analyst: a context-aware learning tool for analytics of learning processes taking place in a web-based learning environment
  • SAM: a Student Activity Monitor intended for personal learning environments
  • SNAPP: a learning analytics tool that visualizes the network of interactions resulting from discussion forum posts and replies
  • Solutionpath StREAM: A leading UK based real-time system that leverage predictive models to determine all facets of student engagement using structured and unstructured sources for all institutional roles
  • Student Success System: a predictive learning analytics tool that predicts student performance and plots learners into risk quadrants based upon engagement and performance predictions, and provides indicators to develop understanding as to why a learner is not on track through visualizations such as the network of interactions resulting from social engagement (e.g. discussion posts and replies), performance on assessments, engagement with content, and other indicators
  • Epistemic Network Analysis (ENA) web tool: An interactive online tool that allow researchers to upload the coded dataset and create the model by specifying units, conversations and codes. Useful functions within the online tool includes mean rotation for comparison between two groups, specifying the sliding window size for connection accumulation, weighed or unweighted models, and parametric and non-parametric statistical testings with suggested write-up and so on. The web tool is stable and open source.

Ethics and privacy

The ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for learning analytics, with concerns raised regarding:

  • Data ownership
  • Communications around the scope and role of learning analytics
  • The necessary role of human feedback and error-correction in learning analytics systems
  • Data sharing between systems, organisations, and stakeholders
  • Trust in data clients

As Kay, Kom and Oppenheim point out, the range of data is wide, potentially derived from:

  • Recorded activity: student records, attendance, assignments, researcher information (CRIS)
  • Systems interactions: VLE, library / repository search, card transactions
  • Feedback mechanisms: surveys, customer care
  • External systems that offer reliable identification such as sector and shared services and social networks

Thus the legal and ethical situation is challenging and different from country to country, raising implications for:

  • Variety of data: principles for collection, retention and exploitation
  • Education mission: underlying issues of learning management, including social and performance engineering
  • Motivation for development of analytics: mutuality, a combination of corporate, individual and general good
  • Customer expectation: effective business practice, social data expectations, cultural considerations of a global customer base.
  • Obligation to act: duty of care arising from knowledge and the consequent challenges of student and employee performance management

In some prominent cases like the inBloom disaster, even full functional systems have been shut down due to lack of trust in the data collection by governments, stakeholders and civil rights groups. Since then, the learning analytics community has extensively studied legal conditions in a series of experts workshops on "Ethics & Privacy 4 Learning Analytics" that constitute the use of trusted learning analytics. Drachsler & Greller released an 8-point checklist named DELICATE that is based on the intensive studies in this area to demystify the ethics and privacy discussions around learning analytics.

  1. D-etermination: Decide on the purpose of learning analytics for your institution.
  2. E-xplain: Define the scope of data collection and usage.
  3. L-egitimate: Explain how you operate within the legal frameworks, refer to the essential legislation.
  4. I-nvolve: Talk to stakeholders and give assurances about the data distribution and use.
  5. C-onsent: Seek consent through clear consent questions.
  6. A-nonymise: De-identify individuals as much as possible
  7. T-echnical aspects: Monitor who has access to data, especially in areas with high staff turn-over.
  8. E-xternal partners: Make sure externals provide highest data security standards

It shows ways to design and provide privacy conform learning analytics that can benefit all stakeholders. The full DELICATE checklist is publicly available.

Privacy management practices of students have shown discrepancies between one's privacy beliefs and one's privacy related actions. Learning analytic systems can have default settings that allow data collection of students if they do not choose to opt-out. Some online education systems such as edX or Coursera do not offer a choice to opt-out of data collection. In order for certain learning analytics to function properly, these systems utilize cookies to collect data.

Open learning analytics

In 2012, a systematic overview on learning analytics and its key concepts was provided by Professor Mohamed Chatti and colleagues through a reference model based on four dimensions, namely:

  • data, environments, context (what?),
  • stakeholders (who?),
  • objectives (why?), and
  • methods (how?).

Chatti, Muslim and Schroeder note that the aim of open learning analytics (OLA) is to improve learning effectiveness in lifelong learning environments. The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model.

Inequality (mathematics)

From Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Inequality...