Digital media in education refers to the use of digital technologies to support and enhance teaching and learning processes. This includes the application of multiple digital software applications, devices, and online platforms as tools for learning. Learners interact with these technologies to access, analyze, evaluate, and create media content and communication in various forms. The integration of digital media in education has dramatically increased over time, significantly transforming traditional educational practices. When viewed through a global and inclusive lens, digital education should be guided by principles of equity, inclusion, and public infrastructure to ensure meaningful participation of all learners. == History == === 20th century === Technological advances in the 20th century, particularly the invention of the Internet, laid the foundation for incorporating technology into education. In the early 1900s, the overhead projector and instructional radio broadcasts were among the first technologies used for educational purposes. The introduction of computers in classrooms occurred in 1950, when a flight simulation program was developed to train pilots at the Massachusetts Institute of Technology. However, access to computers remained extremely limited for several decades. In 1964, John Kemeny and Thomas Kurtz developed the BASIC programming language, which simplified computer interaction and introduced time-sharing, enabling multiple users to work on the same system simultaneously. This innovation made computing increasingly accessible for educational settings. By the 1980s, schools began to show more interest in computers as companies released mass-market devices to the public. Networking further enabled the interconnection of computers into unified communication systems, which proved more efficient and cost-effective than previous stand-alone machines. This development prompted wider adoption of computing in educational institutions. The invention of the World Wide Web in 1992 further simplified internet navigation and sparked further interest in educational settings. Initially, computers were integrated into school curricula for tasks such as word processing, spreadsheet creation, and data organization. By the late 1990s, the Internet became a research tool, functioning as a vast library. By 1999, 99% of public school teachers in the United States reported having access to at least one computer in their schools, and 84% had a computer available in their classrooms. The emergence of World Wide Web also contributed to the development of learning management systems (LMS), which allowed educators to create online teaching environments for content storage, student activities, discussions, and assignments. Advances in digital compression and high-speed Internet made video creation and distribution more affordable, fostering the use of the systems designed for recording lectures. These tools were often incorporated into learning management platforms, supporting the expansion of fully online courses. === 21st century === By 2002, the Massachusetts Institute of Technology began offering recorded lectures to the public, marking a significant milestone in the movement toward accessible online education. The launch of YouTube in 2005 further transformed educational content distribution. Educators increasingly uploaded lectures and instructional videos on platforms with initiatives like Khan Academy, which was active in 2006, contributing to You Tube's role as a prominent educational resource. In 2007, Apple launched iTunesU, another platform for sharing educational resources and videos. Meanwhile, learning management systems gained popularity, with Blackboard and Canvas becoming two of the most widely used platforms with Canvas's release in 2008. That same year also marked the introduction of the first Massive Open Online Course (MOOC), which provided open access to webinars and expert-led instructions for global learners. As technology evolved, traditional projectors were gradually replaced by interactive whiteboards, which enabled educators to integrate digital tools more effectively in their classrooms. By 2009, 97% of classrooms in the United States had at least one computer, and 93% had Internet access. The COVID-19 pandemic, which forced schools across the world to close, significantly impacted education with schools shifting to distance education. Students attended classes remotely using devices such as laptops, phones, and tablets, supported by digital platforms that facilitated at-home learning environments. However, adapting assessment methods to the new learning environment posed certain challenges. A study conducted by Eddie M. Mulenga and José M. Marbán on Zambian students during the pandemic revealed difficulties in adapting to digital learning, particularly in subjects like mathematics. Similar issues were reported among students in Romania, where the transition to virtual learning presented significant obstacles in engagement and adaptability. === Post-pandemic developments === In the period following the onset of COVID-19, education systems worldwide rapidly adopted digital solutions to maintain continuity of learning and teaching. By the end of March 2020, all 46 OECD and partners countries closed some or all of their schools nationwide. By June 2020, the length of school closures in these countries ranged from 7 to over 18 weeks. These disruptions in formal education prompted governments and educators to quickly adopt digital learning. This global shift to online education highlighted considerable inequalities in digital access, although many systems struggled with inequitable access, especially in regions lacking devices, stable internet connections, or conducive home learning environments. Stimultaneously, commercial educational technology (ed-tech) companies introduced rapid digital solutions to the disruption caused by the pandemic. This led to what has been described as a "seller's market," where the urgency of implementation may cause the prioritization of availability and scale over pedagogical and equity considerations. In the post-pandemic era, digital media in education continues to evolve. It increasingly intersects with artificial intelligence (AI) technologies such as adaptive learning platforms, AI-enabled content generation, and personalized learning environments. These tools enhance global engagement and access but also raise concerns about infrastructure, inclusivity, ethical implementation as well as critical pedagogies. Scholars recommend that educators and policymakers adopt inclusive practices, prioritize equitable infrastructure, and develop critical digital literacy. Facer and Selwyn also emphasize the need for public digital infrastructure and sustainable and justice-oriented policies that empower all learners. Overall, these perspectives reflect a growing consensus that digital media in education should be implemented critically to promote inclusive, multimodal, and future-oriented learning environments.
Quack.com
Quack.com was an early voice portal company. The domain name later was used for Quack, an iPad search application from AOL. == History == It was founded in 1998 by Steven Woods, Jeromy Carriere and Alex Quilici as a Pittsburgh, Pennsylvania, USA, based voice portal infrastructure company named Quackware. Quack was the first company to try to create a voice portal: a consumer-based destination "site" in which consumers could not only access information by voice alone, but also complete transactions. Quackware launched a beta phone service in 1999 that allowed consumers to purchase books from sites such as Amazon and CDs from sites such as CDNow by answering a short set of questions. Quack followed with a set of information services from movie listings (inspired by, but expanding upon, Moviefone) to news, weather and stock quotes. This concept introduced a series of lookalike startups including Tellme Networks which raised more money than any Internet startup in history on a similar concept. Quack received its first venture funding from HDL Capital in 1999 and moved operations to Mountain View in Silicon Valley, California in 1999. A deal with Lycos was announced in May 2000. In September 2000 Quack was acquired for $200 million by America Online (AOL) and moved onto the Netscape campus with what was left of the Netscape team. Quack was attacked in the Canadian press for being representative of the Canadian "brain drain" to the US during the Internet bubble, focusing its recruiting efforts on the University of Waterloo, hiring more than 50 engineers from Waterloo in less than 10 months. Quack competitor Tellme Networks raised enormous funds in what became a highly competitive market in 2000, with the emergence of more than a dozen additional competitors in a 12-month period. Following its acquisition by America Online in an effort led by Ted Leonsis to bring Quack into AOL Interactive, the Quack voice service became AOLbyPhone as one of AOL's "web properties" along with MapQuest, Moviefone and others. Quack secured several patents that underlie the technical challenges of delivering interactive voice services. Constructing a voice portal required integrations and innovations not only in speech recognition and speech generation, but also in databases, application specification, constraint-based reasoning and artificial intelligence and computational linguistics. "Quack"'s name derived from the company goal of providing not only voice-based services, but more broadly "Quick Ubiquitous Access to Consumer Knowledge". The patents assigned to Quack.com include: System and method for voice access to Internet-based information, System and method for advertising with an Internet Voice Portal and recognizing the axiom that in interactive voice systems one must "know the set of possible answers to a question before asking it". System and method for determining if one web site has the same information as another web site. Quack.com was spoofed in The Simpsons in March 2002 in the episode "Blame It on Lisa" in which a "ComQuaak" sign is replaced by another equally crazy telecom company name. == 2010 onwards == In July 2010, quack.com became the focus of a new AOL iPad application, that was a web search experience. The product delivers web results and blends in picture, video and Twitter results. It enables you to preview the web results before you go to the site, search within each result, and flip through the results pages, making full use of the iPad's touch screen features. The iPad app was free via iTunes, but support discontinued in 2012.
Operational database
Operational database management systems (also referred to as OLTP databases or online transaction processing databases), are used to update data in real-time. These types of databases allow users to do more than simply view archived data. Operational databases allow you to modify that data (add, change or delete data), doing it in real-time. OLTP databases provide transactions as main abstraction to guarantee data consistency that guarantee the so-called ACID properties. Basically, the consistency of the data is guaranteed in the case of failures and/or concurrent access to the data. == History == Since the early 1990s, the operational database software market has been largely taken over by SQL engines. In 2014, the operational DBMS market (formerly OLTP) was evolving dramatically, with new, innovative entrants and incumbents supporting the growing use of unstructured data and NoSQL DBMS engines, as well as XML databases and NewSQL databases. NoSQL databases typically have focused on scalability and have renounced to data consistency by not providing transactions as OLTP system do. Operational databases are increasingly supporting distributed database architecture that can leverage distribution to provide high availability and fault tolerance through replication and scale out ability. The growing role of operational databases in the IT industry is moving fast from legacy databases to real-time operational databases capable to handle distributed web and mobile demand and to address Big data challenges. Recognizing this, Gartner started to publish the Magic Quadrant for Operational Database Management Systems in October 2013. == List of operational databases == Notable operational databases include: == Use in business == Operational databases are used to store, manage and track real-time business information. For example, a company might have an operational database used to track warehouse/stock quantities. As customers order products from an online web store, an operational database can be used to keep track of how many items have been sold and when the company will need to reorder stock. An operational database stores information about the activities of an organization, for example customer relationship management transactions or financial operations, in a computer database. Operational databases allow a business to enter, gather, and retrieve large quantities of specific information, such as company legal data, financial data, call data records, personal employee information, sales data, customer data, data on assets and many other information. An important feature of storing information in an operational database is the ability to share information across the company and over the Internet. Operational databases can be used to manage mission-critical business data, to monitor activities, to audit suspicious transactions, or to review the history of dealings with a particular customer. They can also be part of the actual process of making and fulfilling a purchase, for example in e-commerce. == Data warehouse terminology == In data warehousing, the term is even more specific: the operational database is the one which is accessed by an operational system (for example a customer-facing website or the application used by the customer service department) to carry out regular operations of an organization. Operational databases usually use an online transaction processing database which is optimized for faster transaction processing (create, read, update and delete operations). An operational database is the source for a data warehouse. Data from an operational database can be loaded into an operational data store at a data warehouse before the data is processed into the data warehouse.
Literature review
A literature review is an overview of previously published works on a particular topic. The term can refer to a full scholarly paper or a section of a scholarly work such as books or articles. Either way, a literature review provides the researcher/author and the audiences with general information of an existing knowledge of a particular topic. A good literature review has a proper research question, a proper theoretical framework, and/or a chosen research method. It serves to situate the current study within the body of the relevant literature and provides context for the reader. In such cases, the review usually precedes the methodology and results sections of the work. Producing a literature review is often part of a graduate and post-graduate requirement, included in the preparation of a thesis, dissertation, or a journal article. Literature reviews are also common in a research proposal or prospectus (the document approved before a student formally begins a dissertation or thesis). A literature review can be a type of a review article. In this sense, it is a scholarly paper that presents the current knowledge including substantive findings as well as theoretical and methodological contributions to a particular topic. Literature reviews are secondary sources and do not report new or original experimental work. Most often associated with academic-oriented literature, such reviews are found in academic journals and are not to be confused with book reviews, which may also appear in the same publication. Literature reviews are a basis for research in nearly every academic field. == Types == Since the concept of a systematic review was formalized in the 1970s, a basic division among types of reviews is the dichotomy of narrative reviews versus systematic reviews. The main types of narrative reviews are evaluative, exploratory, and instrumental. A fourth type of review of literature (the scientific literature) is the systematic review but it is not called a literature review, which absent further specification, conventionally refers to narrative reviews. A systematic review focuses on a specific research question to identify, appraise, select, and synthesize all high-quality research evidence and arguments relevant to that question. A meta-analysis is typically a systematic review using statistical methods to effectively combine the data used on all selected studies to produce a more reliable result. Torraco (2016) describes an integrative literature review. The purpose of an integrative literature review is to generate new knowledge on a topic through the process of review, critique, and synthesis of the literature under investigation. George et al (2023) offer an extensive overview of review approaches. They also propose a model for selecting an approach by looking at the purpose, object, subject, community, and practices of the review. They describe six different types of review, each with their own unique purposes: Exploratory or scoping reviews focus on breadth as opposed to depth Systematic or integrative reviews integrate empirical studies on a topic Meta-narrative reviews are qualitative and use literature to compare research or practice communities Problematizing or critical reviews propose new perspectives on a concept by association with other literature Meta-analyses and meta-regressions integrate quantitative studies and identify moderators Mixed research syntheses combine other review approaches in the same paper == Process and product == Shields and Rangarajan (2013) distinguish between the process of reviewing the literature and a finished work or product known as a literature review. The process of reviewing the literature is often ongoing and informs many aspects of the empirical research project. The process of reviewing the literature requires different kinds of activities and ways of thinking. Shields and Rangarajan (2013) and Granello (2001) link the activities of doing a literature review with Benjamin Bloom's revised taxonomy of the cognitive domain (ways of thinking: remembering, understanding, applying, analyzing, evaluating, and creating). === Use of artificial intelligence in a literature review === Artificial intelligence (AI) is reshaping traditional literature reviews across various disciplines. Generative pre-trained transformers, such as ChatGPT, are often used by students and academics for review purposes. Since 2023, an increasing number of tools powered by large language models and other artificial intelligence technologies have been developed to assist, automate, or generate literature reviews. Nevertheless, the employment of ChatGPT in academic reviews is problematic due to ChatGPT's propensity to "hallucinate". In response, efforts are being made to mitigate these hallucinations through the integration of plugins. For instance, Rad et al. (2023) used ScholarAI for review in cardiothoracic surgery.
Flajolet–Martin algorithm
The Flajolet–Martin algorithm is an algorithm for approximating the number of distinct elements in a stream with a single pass and space-consumption logarithmic in the maximal number of possible distinct elements in the stream (the count-distinct problem). The algorithm was introduced by Philippe Flajolet and G. Nigel Martin in their 1984 article "Probabilistic Counting Algorithms for Data Base Applications". Later it has been refined in "LogLog counting of large cardinalities" by Marianne Durand and Philippe Flajolet, and "HyperLogLog: The analysis of a near-optimal cardinality estimation algorithm" by Philippe Flajolet et al. In their 2010 article "An optimal algorithm for the distinct elements problem", Daniel M. Kane, Jelani Nelson and David P. Woodruff give an improved algorithm, which uses nearly optimal space and has optimal O(1) update and reporting times. == The algorithm == Assume that we are given a hash function h a s h ( x ) {\displaystyle \mathrm {hash} (x)} that maps input x {\displaystyle x} to integers in the range [ 0 ; 2 L − 1 ] {\displaystyle [0;2^{L}-1]} , and where the outputs are sufficiently uniformly distributed. Note that the set of integers from 0 to 2 L − 1 {\displaystyle 2^{L}-1} corresponds to the set of binary strings of length L {\displaystyle L} . For any non-negative integer y {\displaystyle y} , define b i t ( y , k ) {\displaystyle \mathrm {bit} (y,k)} to be the k {\displaystyle k} -th bit in the binary representation of y {\displaystyle y} , such that: y = ∑ k ≥ 0 b i t ( y , k ) 2 k . {\displaystyle y=\sum _{k\geq 0}\mathrm {bit} (y,k)2^{k}.} We then define a function ρ ( y ) {\displaystyle \rho (y)} that outputs the position of the least-significant set bit in the binary representation of y {\displaystyle y} , and L {\displaystyle L} if no such set bit can be found as all bits are zero: ρ ( y ) = { min { k ≥ 0 ∣ b i t ( y , k ) ≠ 0 } y > 0 L y = 0 {\displaystyle \rho (y)={\begin{cases}\min\{k\geq 0\mid \mathrm {bit} (y,k)\neq 0\}&y>0\\L&y=0\end{cases}}} Note that with the above definition we are using 0-indexing for the positions, starting from the least significant bit. For example, ρ ( 13 ) = ρ ( 1101 2 ) = 0 {\displaystyle \rho (13)=\rho (1101_{2})=0} , since the least significant bit is a 1 (0th position), and ρ ( 8 ) = ρ ( 1000 2 ) = 3 {\displaystyle \rho (8)=\rho (1000_{2})=3} , since the least significant set bit is at the 3rd position. At this point, note that under the assumption that the output of our hash function is uniformly distributed, then the probability of observing a hash output ending with 2 k {\displaystyle 2^{k}} (a one, followed by k {\displaystyle k} zeroes) is 2 − ( k + 1 ) {\displaystyle 2^{-(k+1)}} , since this corresponds to flipping k {\displaystyle k} heads and then a tail with a fair coin. Now the Flajolet–Martin algorithm for estimating the cardinality of a multiset M {\displaystyle M} is as follows: Initialize a bit-vector BITMAP to be of length L {\displaystyle L} and contain all 0s. For each element x {\displaystyle x} in M {\displaystyle M} : Calculate the index i = ρ ( h a s h ( x ) ) {\displaystyle i=\rho (\mathrm {hash} (x))} . Set B I T M A P [ i ] = 1 {\displaystyle \mathrm {BITMAP} [i]=1} . Let R {\displaystyle R} denote the smallest index i {\displaystyle i} such that B I T M A P [ i ] = 0 {\displaystyle \mathrm {BITMAP} [i]=0} . Estimate the cardinality of M {\displaystyle M} as 2 R / ϕ {\displaystyle 2^{R}/\phi } , where ϕ ≈ 0.77351 {\displaystyle \phi \approx 0.77351} . The idea is that if n {\displaystyle n} is the number of distinct elements in the multiset M {\displaystyle M} , then B I T M A P [ 0 ] {\displaystyle \mathrm {BITMAP} [0]} is accessed approximately n / 2 {\displaystyle n/2} times, B I T M A P [ 1 ] {\displaystyle \mathrm {BITMAP} [1]} is accessed approximately n / 4 {\displaystyle n/4} times and so on. Consequently, if i ≫ log 2 n {\displaystyle i\gg \log _{2}n} , then B I T M A P [ i ] {\displaystyle \mathrm {BITMAP} [i]} is almost certainly 0, and if i ≪ log 2 n {\displaystyle i\ll \log _{2}n} , then B I T M A P [ i ] {\displaystyle \mathrm {BITMAP} [i]} is almost certainly 1. If i ≈ log 2 n {\displaystyle i\approx \log _{2}n} , then B I T M A P [ i ] {\displaystyle \mathrm {BITMAP} [i]} can be expected to be either 1 or 0. The correction factor ϕ ≈ 0.77351 {\displaystyle \phi \approx 0.77351} (OEIS: A244256) is found by calculations, which can be found in the original article. == Improving accuracy == A problem with the Flajolet–Martin algorithm in the above form is that the results vary significantly. A common solution has been to run the algorithm multiple times with k {\displaystyle k} different hash functions and combine the results from the different runs. One idea is to take the mean of the k {\displaystyle k} results together from each hash function, obtaining a single estimate of the cardinality. The problem with this is that averaging is very susceptible to outliers (which are likely here). A different idea is to use the median, which is less prone to be influences by outliers. The problem with this is that the results can only take form 2 R / ϕ {\displaystyle 2^{R}/\phi } , where R {\displaystyle R} is integer. A common solution is to combine both the mean and the median: Create k ⋅ l {\displaystyle k\cdot l} hash functions and split them into k {\displaystyle k} distinct groups (each of size l {\displaystyle l} ). Within each group use the mean for aggregating together the l {\displaystyle l} results, and finally take the median of the k {\displaystyle k} group estimates as the final estimate. The 2007 HyperLogLog algorithm splits the multiset into subsets and estimates their cardinalities, then it uses the harmonic mean to combine them into an estimate for the original cardinality.
Shaded Picture System
The Shaded Picture System was a 3D raster computer display processor introduced by Evans & Sutherland in October 1973. The Shaded Picture System was the first general-purpose, commercially available raster computer graphics display processor capable of real-time, shaded 3D graphics. It could only display black and white graphics at a resolution of 256 by 256. It was extremely expensive, and very few units were ever sold. == History == The principles of shaded, hidden-line true 3D graphics were pioneered at the University of Utah in 1967. However, this algorithm was slow and would take several minutes to produce an image. In 1970, Gary Watkins developed a FORTRAN simulator of a faster algorithm that would theoretically generate shaded 3D images in real-time, "if implemented in suitable hardware". The simulator itself was still not capable of real-time shaded 3D image rendering. Evans & Sutherland developed a functional prototype of this "suitable hardware", which was later sold as the Shaded Picture System in 1973. About a year earlier in 1972, Evans & Sutherland sold the first and only CT1 to Case Western Reserve University. The CT1, or Continuous Tone 1, was a specialized image generator, not meant as a marketable or mass-produced product. At the time, the CT1, along with G.E./NASA's upgraded Electronic Scene Generator from 1971, would have been the only real-time raster graphics systems sold to customers comparable to the Shaded Picture System, although both the CT1 and Electronic Scene Generator were intentionally produced as one-off products and specialized for the needs of their customers. The Shaded Picture System, in contrast, was intentionally marketed.In early 1975, Evans & Sutherland demonstrated a random-access video frame buffer using relatively low-cost semiconductor memory, which was much more capable than the Shaded Picture System. When interfaced with a (non-shaded) E&S Picture System, the frame buffer had a resolution of 512 by 512 in grayscale and partial color capabilities. By the end of 1975, this frame buffer was commercially available.
Data Science Africa
Data Science Africa (DSA) is a non-profit knowledge sharing professional group that aims at bringing together leading researchers and practitioners working on data science methods or applications relevant to Africa, and providing training on state of the art data science methods to students and others interested in developing practical skills. Since 2013, DSA has been organizing conference, workshops and summer schools on machine learning and data science across East Africa. Facilitators of Summer School and workshops are researchers and practitioners from the academia, private and public institutions across the world. == Summer schools and workshops == The first summer school which started as Gaussian Process Summer School was held at Makerere University in Kampala, Uganda from 6th to 9 August 2013. The First Data Science Summer School and Workshop was held at Dedan Kimathi University of Technology in Nyeri, Kenya from 15th to 19 June 2015. The Second Data Science Summer School was held at Makerere University, Kampala, Uganda from 27th to 29 July 2016, and the workshop was held at Pulse Lab, Kampala, Uganda from 30 July to 1 August 2016. The Third Data Science Summer School and Workshop was held at Nelson Mandela African Institute of Science and Technology, Tanzania from 19th to 21 July 2017. Among the sponsors of the event was ARM