Relevant bibliographies by topics / Players' learning

Academic literature on the topic 'Players' learning'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Players' learning"

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Bojanić, Milana, and Goran Bojanić. "Self-Learning Mechanism for Mobile Game Adjustment towards a Player." Applied Sciences 11, no. 10 (May 13, 2021): 4412. http://dx.doi.org/10.3390/app11104412.

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Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%.
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Wu, Tianhang, Hanchen Wang, Jian Yang, Liang Xu, Yumeng Li, and Jun Zhang. "The prisoner’s dilemma game on scale-free networks with heterogeneous imitation capability." International Journal of Modern Physics C 29, no. 09 (September 2018): 1850077. http://dx.doi.org/10.1142/s0129183118500778.

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In human societies, personal heterogeneity may affect the strategy adoption capability of the individuals. In this paper, we study the effects of heterogeneous learning ability on the evolution of cooperation by introducing heterogeneous imitation capability of players. We design a pre-factor [Formula: see text] to represent the heterogeneous learning ability of players, which is related to the degree of players. And a parameter [Formula: see text] is used to tune the learning levels. If [Formula: see text], the learning ability of players decreases and the low-degree player has the higher reduction level, but if [Formula: see text], the learning ability of low-degree players enhances to a higher level. By carrying out extensive simulations, it reveals that the evolution of cooperation is influenced significantly by introducing player’s heterogeneous learning ability and can be promoted under the right circumstances. This finding sheds some light on the important effect of individual heterogeneity on the evolutionary game.
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Wolitzky, Alexander. "Learning from Others' Outcomes." American Economic Review 108, no. 10 (October 1, 2018): 2763–801. http://dx.doi.org/10.1257/aer.20170914.

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I develop a simple model of social learning in which players observe others’ outcomes but not their actions. A continuum of players arrives continuously over time, and each player chooses once-and-for-all between a safe action (which succeeds with known probability) and a risky action (which succeeds with fixed but unknown probability, depending on the state of the world). The actions also differ in their costs. Before choosing, a player observes the outcomes of K earlier players. There is always an equilibrium in which success is more likely in the good state, and this alignment property holds whenever the initial generation of players is not well informed about the state. In the case of an outcome-improving innovation (where the risky action may yield a higher probability of success), players take the correct action as K → ∞. In the case of a cost-saving innovation (where the risky action involves saving a cost but accepting a lower probability of success), inefficiency persists as K → ∞ in any aligned equilibrium. Whether inefficiency takes the form of under-adoption or over-adoption also depends on the nature of the innovation. Convergence of the population to equilibrium may be nonmonotone. (JEL D81, D83, O32, Q12, Q16)
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Silva, João Vítor Rocha da, and Paulo Canas Rodrigues. "All-NBA Teams’ Selection Based on Unsupervised Learning." Stats 5, no. 1 (February 9, 2022): 154–71. http://dx.doi.org/10.3390/stats5010011.

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All-NBA Teams’ selections have great implications for the players’ and teams’ futures. Since contract extensions are highly related to awards, which can be seen as indexes that measure a players’ production in a year, team selection is of mutual interest for athletes and franchises. In this paper, we are interested in studying the current selection format. In particular, this study aims to: (i) identify the factors that are taken into consideration by voters when choosing the three All-NBA Teams; and (ii) suggest a new selection format to evaluate players’ performances. Average game-related statistics of all active NBA players in regular seasons from 2013-14 to 2018-19, were analyzed using LASSO (Logistic) Regression and Principal Component Analysis (PCA). It was possible: (i) to determine an All-NBA player profile; (ii) to determine that this profile can cause a misrepresentation of players’ modern and versatile gameplay styles; and (iii) to suggest a new way to evaluate and select players, through PCA. As the results of this paper a model is presented that may help not only the NBA to better evaluate players, but any basketball league; it also may be a source to researchers that aim to investigate player performance, development, and their impact over many seasons.
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Hurkens, Sjaak. "Learning by Forgetful Players." Games and Economic Behavior 11, no. 2 (November 1995): 304–29. http://dx.doi.org/10.1006/game.1995.1053.

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Li, Neill Y., Nicholas J. Lemme, Steven F. Defroda, Elvis Nunez, Davis A. Hartnett, and Brett D. Owens. "Performance After Operative Versus Nonoperative Management of Shoulder Instability in the National Basketball Association." Orthopaedic Journal of Sports Medicine 7, no. 12 (December 1, 2019): 232596711988933. http://dx.doi.org/10.1177/2325967119889331.

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Background: Although nonoperative management after shoulder instability injury allows an athlete to return to play sooner than operative intervention, higher rates of recurrence have been observed after nonoperative management. However, no study has investigated the differences in performance of National Basketball Association (NBA) players after index shoulder instability events managed nonoperatively versus operatively. Purpose/Hypothesis: The purpose of this study was to identify shoulder instability events in NBA athletes and assess differences in performance after injury with nonoperative versus operative management. We hypothesized that players who undergo operative intervention have reduced risk of recurrence and are able to continue their elite level of play as opposed to those who undergo nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: Publicly available injury data from the 1986-1987 through 2017-2018 seasons were reviewed to identify NBA athletes sustaining a shoulder instability event. In addition to characteristics, player performance information, including games played, player efficiency rating (PER), and win shares, was analyzed before and for 3 seasons after injury. Statistical learning models were applied to identify performance variables that have the greatest predictive value to determine players who would benefit from surgery. Results: A total of 60 players with shoulder instability events were identified between 1986 and 2018. After injury, 37 players (61.7%) eventually underwent surgery and 23 players (38.3%) did not. Players who were treated nonoperatively had significantly decreased PER, games played, and offensive win shares in the season after injury ( P < .05). Players who underwent surgery did not see a decline in PER, games played, or win shares. Random forest modeling found that true shooting percentage and win shares per 48 minutes were the performance variables most predictive in determining which players would benefit from surgery after shoulder instability. Conclusion: Players who underwent surgical intervention for shoulder instability maintained their PER, games played, and win share performance characteristics, whereas players who did not undergo surgery had declines in these parameters. Given the demands of shoulder function in basketball and the risk of recurrence after an instability event, surgery enhances a player’s opportunity to maintain a high level of performance after injury.
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Santillan, Luis Alberto Casillas, and Johor Ismael Jara Gonzalez. "Learning Avatar's Locomotion Patterns Through Spatial Analysis in FPS Video Games." International Journal of Organizational and Collective Intelligence 8, no. 1 (January 2018): 28–45. http://dx.doi.org/10.4018/ijoci.2018010103.

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This article describes how current video games offer an extreme use of media fusion. Such construction implies a novel form of complexity regarding game control and active response from game to player. All of these elements produce deeper immersion effect in players. In order to perform a detailed supervision over this kind of game, additional controls should be included in game. Some of these controls are the moving and decision schemes. Authors believe that players move around virtual scenarios following some sort of pattern. Every player would have a specific pattern, according to his/her experience and capability to manage the gamepad layout. Current proposal consists in a 3D geometrical model surrounding player's avatar. Data unwittingly provided by the player, have elements to discover and, eventually, learn some gamers' patterns. The availability of these patterns would allow an improved game response and even the possibility of machine learning, as well as other artificial intelligence strategies. Every 3D game may include the model proposed in this paper, due to its noninvasive operation.
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Karnuta, Jaret M., Bryan C. Luu, Heather S. Haeberle, Paul M. Saluan, Salvatore J. Frangiamore, Kim L. Stearns, Lutul D. Farrow, et al. "Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017." Orthopaedic Journal of Sports Medicine 8, no. 11 (November 1, 2020): 232596712096304. http://dx.doi.org/10.1177/2325967120963046.

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Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player's future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury’s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers.
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Hernandez, Jérôme, Mathieu Muratet, Matthis Pierotti, and Thibault Carron. "Can We Detect Non-playable Characters’ Personalities Using Machine And Deep Learning Approaches?" European Conference on Games Based Learning 16, no. 1 (September 29, 2022): 271–79. http://dx.doi.org/10.34190/ecgbl.16.1.627.

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Personality recognition and computational psychometrics data have become prevalent in personnel selection processes. Such assessment tools are adequate for human resources seeking tools to assess a large volume of diverse player personalities in the current "war of talents." Recently, studies about using Gamified situational judgment test approaches have shown positive results in assessing players' behavior and personality. Gamified situational judgment tests combine the advantages of gamification, such as enhancing players' reactions and flow state, with the acknowledged traditional situational judgment test approach. To gamify a situational judgment test, an innovative approach using the visual novel game genre has shown positive results in the gamification by adding game elements such as narrative scripts, non-player characters, dialogs, and audiovisual assets to the test. Indeed, these elements play an essential role in the validity of the players' personality results by using a stealth-assessment method to minimize social bias and player's stress. However, to our knowledge, as gamification in personality detection is still recent, little is known on the possible positive outcomes of designing game elements such as the dialogues and non-player character personalities in the validity of the team cohesion measure. To this end, we propose an empirical study to build personality trait models based on non-players characters' speeches. We used the Myers–Briggs Type Indicator based on four dichotomies to classify the personalities as one of companies and organizations' most used personality typology. For each of the four dimensions, we train twenty-four separate binary classifiers and one 16-class classifier, using well-established machine learning and a convolutional neural network in the domain of natural language processing, text analytics, and computational psychometrics. The results of this study show that it is possible to recognize non-playable characters’ personalities and thus can help game designers to understand their characters' personalities using natural language processing.
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Stanley, Kenneth, Ryan Cornelius, Risto Miikkulainen, Thomas D’Silva, and Aliza Gold. "Real-Time Learning in the NERO Video Game." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 1, no. 1 (September 28, 2021): 159–60. http://dx.doi.org/10.1609/aiide.v1i1.18736.

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If game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. The real-time NeuroEvolution of Augmenting Topologies (rtNEAT) method, which can evolve increasingly complex artificial neural networks in real time as a game is being played, will be presented. The rtNEAT method makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. In order to demonstrate this concept, the NeuroEvolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. The live demo will show how agents in NERO adapt in real time as they interact with the player. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games.
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Dissertations / Theses on the topic "Players' learning"

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Kallunki, K. P. (Kari-Pekka). "Learning English in World of Warcraft:perspectives from the players." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201603111310.

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This thesis seeks to investigate how an online video game called ‘World of Warcraft’ facilitates learning English as a second language and provide a critical analysis of both advantages and disadvantages of this type of informal language learning. The relevance for this study stems from the fact that playing video games has risen in popularity since the beginning of 2000s, and approximately 35 percent of the playerbase in World of Warcraft, for example, are female players. This means that more and more people are learning English from video games. The data for this study has been gathered by conducting a survey for European World of Warcraft players, asking them to share their thoughts, opinions and experiences on learning English in World of Warcraft. The results of the survey are analysed from the perspective of formal English education in order to evaluate whether the language in video games pose a threat to the objectives of formal language education or if there is something that formal teaching could take from learning English in video games. The aims of formal language education have started to shift from native-like proficiency towards achieving communicational competency. Networked online video games are one of the most accessible ways to converse and interact with people from all over the world. Hampel (2006) and Furstenberg (1997) say that computer-assisted language learning provides the learners opportunities to actively participate and collaborate with their peers in language learning situations without the need for physical attendance. Sylvén and Sundqvist (2012) say that World of Warcraft provides its players authentic situations to hear and practice English. According to Gee (2003, 2007), video games inspire genuine motivation, which alongside authenticity is something that schools often have difficulties to reproduce. Gee (2007) also says that video games are an effective platform for learning because the learning in them becomes ‘situated’, meaning that the language in the game is constantly supported by the visual and auditory clues and actions from the player. Saarenkunnas (2006) notes that this is why children are quite adeptly able to play video games in English even at a young age. The results evoke mixed opinions. On one hand, playing World of Warcraft appears to be an immersive and effective way to learn new and even complex vocabulary. World of Warcraft also functions as a social platform for the players to collaborate, interact and communicate with each other. On the other hand, English is mostly spoken as an international language in the game, which means that a learner is exposed to various different Englishes that might not always conform to the basic rules of English spelling, syntax and grammar. Thus, World of Warcraft might not be the best place to learn to speak English correctly, and the level of social engagement and exposure to English language is rather dependent on the player’s own activity and preferences. Toxic language and behavior is often mentioned in the responses, which leads to the question if a game community like this can be considered to be socially encouraging. A question for future research would be how video games can be harnessed for the uses of formal language education while ensuring that only positive effects on learning English are transferred
Tämän Pro Gradu -tutkielman tavoitteena on tutkia, millä tavoin verkossa pelattava moninpeli World of Warcraft edesauttaa englannin oppimista vieraana kielenä. Samalla tavoitteena on kriittisesti arvioida pelaamisen kautta tapahtuvan kielenoppimisen hyötyjä ja haittoja. Videopelien pelaaminen on yleistynyt 2000-luvulta lähtien, ja esimerkiksi World of Warcraftin pelaajakunnasta noin 35 prosenttia on naispuolisia pelaajia. Videopeleillä on alati laajeneva kohderyhmä, ja englantia opitaan yhä enemmän pelien kautta, joten on aiheellista tutkia kielenoppimista peleissä kouluopetuksen näkökulmasta. Tämä tutkimus perustuukin eurooppalaisilla World of Warcraftin pelaajilla teetettyyn kyselyyn, jossa heitä pyydettiin kertomaan ajatuksiaan, mielipiteitään ja kokemuksiaan liittyen englannin oppimiseen World of Warcraftin kautta. Tutkimuksen eräänä tavoitteena on myös selvittää, onko videopelien avulla tapahtuvaa englannin oppimista syytä pitää uhkana koulujärjestelmän asettamien kielenoppimisen tavoitteiden kannalta vai voitaisiinko videopeleistä ottaa jopa vaikutteita kouluopetukseen. Verkossa pelattavat moninpelit ovatkin yksi kätevimmistä keinoista päästä harjoittelemaan englantia kansainvälisessä kontekstissa. Hampel (2006) ja Furstenberg (1997) sanovat, että tietokonepohjaisissa kielenoppimistilanteissa oppija pääsee toimimaan aktiivisena osallistujana ja saa tehdä yhteistyötä muiden oppijoiden kanssa. Sylvén ja Sundqvist (2012) jatkavat, että World of Warcraft tarjoaa pelaajilleen mahdollisuuksia käyttää englantia aidonkaltaisissa vuorovaikutustilanteissa. Gee (2003, 2007) puolestaan sanoo, että videopelit herättävät pelaajassa aitoa motivaatiota oppimista kohtaan. Gee (2007) myös perustelee videopelien tehokkuutta kielenoppimisen välineenä sanomalla, että kieli on peleissä vahvasti kontekstualisoitua. Pelissä suoritetut toiminnot ja pelin tarjoamat kuvalliset ja äänelliset vihjeet siis tukevat kielen ymmärtämistä ja helpottavat oppimista. Saarenkunnas (2006) toteaakin, että tästä syystä myös nuoret lapset kykenevät pelaamaan englanninkielisiä pelejä. Tutkimuksesta saadut tulokset kuitenkin antavat ristiriitaisen kuvan kielenoppimisesta World of Warcraftissa. Toisaalta pelaaminen on helppo ja mielenkiintoinen tapa oppia uutta ja monimutkaista sanastoa. World of Warcraft myös tarjoaa pelaajilleen sosiaalisen ympäristön, jossa harjoitella englannin puhumista autenttisissa kielenkäyttötilanteissa. Toisaalta taas pelaajien puhuman englannin taso vaihtelee puhujakohtaisesti, eikä pelissä puhuttu kieli noudata aina kaikkia englannin kielioppi- tai oikeinkirjoitussääntöjä. Pelissä myös törmää usein epäsoveliaaseen kielenkäyttöön ja käytökseen, joten pelin kannustavuus kielenoppimisympäristönä voidaan kyseenalaistaa. Kouluopetuksessa voitaisiin kuitenkin ottaa mallia videopelien tavasta luoda motivoivia ja autenttisia kielenkäyttötilanteita. Jatkotutkimuksessa voitaisiinkin selvittää, miten videopeleistä saadut kielenoppimisen hyödyt voitaisiin valjastaa kouluopetuksen käyttöön siten, että vain kielenoppimisen kannalta myönteiset vaikutukset siirtyvät pelistä oppijaan
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Lu, Wei-Lwun. "Learning to track and identify players from broadcast sports videos." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/39956.

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Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This thesis introduces the first intelligent system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The tracking system is based on the tracking-by-detection philosophy. We first localize players using a player detector, categorize detections based on team colors, and then group them into tracks of specific players. Instead of using visual cues to distinguish between players, we instead rely on their short-term motion patterns. The homography estimation is solved by using a variant of the Iterated Closest Points (ICP). Unlike most existing algorithms that rely on matching robust feature points, we propose to match edge points in two images. In addition, we also introduce a technique to update the model online to accommodate logos and patterns in different stadiums. The identification system utilizes both visual and spatial cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field. In addition, we propose a novel Linear Programming Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we pioneer the use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training data required. Experiments show that we can use weakly supervised learning with merely 200 labels to achieve similar accuracies to a strongly supervised approach, which requires at least 20000 labels.
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Taylor, Angela Ruth. "Understanding older amateur keyboard players : music learning and mature adult musical identity." Thesis, UCL Institute of Education (IOE), 2009. http://eprints.ioe.ac.uk/20594/.

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Galvao, Afonso Celso Tanus. "Practice in orchestral life : an exploratory study of string players' learning processes." Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269116.

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This study explored the learning habits of string players in a professional symphony orchestra, how they practise, what influences their practice, and how it interacts with rehearsal. For this, four research questions were investigated: () how complementary are rehearsal and practice to the learning of orchestral music? (u) how is the practice of orchestral players approached in terms of methods, regularity, content and structure? (ii) how do such factors as conductor, musical preference, and loss of individuality affect motivation to practise? and (v) what are the strategies that conductors use to influence musicians' commitment to practise? A triangular methodological strategy was undertaken, comprising semi-structured interviews, questionnaire, observation, and case studies. These were preceded by a semantic field research exercise aiming at eliciting the vocabulary participants used in reference to the phenomena investigated. Forty South-American musicians completed a comprehensive mixed-format questionnaire; fifteen musicians and five conductors were interviewed at length; and Twenty-five rehearsals/concerts were observed. Finally, three case studies described musicians' learning processes for a contemporary composition and two traditional pieces of the symphonic repertoire. Results indicated that rehearsal and practice were highly complementary aspects of orchestral learning. In terms of practice habits, players displayed a large range of physical and mental techniques to solve musical problems swiftly, as well as considerable metacognitive capacity. Their practice processes included more general aspects such as planning, evaluation, setting up goals, and priorities; and specific strategies such as listening to and playing with records, speed alteration, use of the metronome, as well as relaxation exercises with and without the instrument. There was also a concern with balancing the practice of technical exercises, studies, solo and orchestral repertoire. However, motivation to learn was found to be affected by the conductor, repertoire and general working ethos, in that order of importance. Conductors were aware of their role as a motivational force behind players' practice, but assumed varying levels of responsibility for this. Implications for current and future research on practice and expertise, and for instrumental music teaching and learning were addressed.
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Vidlund, Anna. "English in video and online computer games : Potential enhancement of players’ vocabulary." Thesis, Linnéuniversitetet, Institutionen för språk (SPR), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28402.

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The aim of this essay is to distinguish whether the activity of playing video and online computer games as a leisure activity could be considered to be a learning situation. With the focus on vocabulary, this study investigates the possibility that gamers could improve their language proficiency while playing video and online computer games. The methodology is based on qualitative observations (Patel & Davidson 2011) and on interviews with seven players of five different games. The observations mainly considered the vocabulary used in the games and how the players used the English language while playing. The interviews are constructed with the methodology mentioned by Kylén (2004). The interview questions aimed to answer if the players had been noticing an improvement of their vocabulary. The data are mainly acquired from the observations and interviews. The background sections build on studies relating to computer-based language learning and on the previous research on ELF, primarily from Barbara Seidlhofer (2011). Even though the data acquired from the observations and interviews are limited, it is apparent that video and online computer games have a noticeable impact on language development, regarding vocabulary. The main conclusion of this study is that the games do not influence the players’ language proficiency as considerably as the engaging in the functions in conjunction with the games.
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Whetzel, Jonathan Hunt. "Developing intelligent agents for training systems that learn their strategies from expert players." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2662.

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Computer-based training systems have become a mainstay in military and private institutions for training people how to perform certain complex tasks. As these tasks expand in difficulty, intelligent agents will appear as virtual teammates or tutors assisting a trainee in performing and learning the task. For developing these agents, we must obtain the strategies from expert players and emulate their behavior within the agent. Past researchers have shown the challenges in acquiring this information from expert human players and translating it into the agent. A solution for this problem involves using computer systems that assist in the human expert knowledge elicitation process. In this thesis, we present an approach for developing an agent for the game Revised Space Fortress, a game representative of the complex tasks found in training systems. Using machine learning techniques, the agent learns the strategy for the game by observing how a human expert plays. We highlight the challenges encountered while designing and training the agent in this real-time game environment, and our solutions toward handling these problems. Afterward, we discuss our experiment that examines whether trainees experience a difference in performance when training with a human or virtual partner, and how expert agents that express distinctive behaviors affect the learning of a human trainee. We show from our results that a partner agent that learns its strategy from an expert player serves the same benefit as a training partner compared to a programmed expert-level agent and a human partner of equal intelligence to the trainee.
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Hill, Jimmy Matthew. "Extra-curricular activities on the field subcultural learning of in-game infractions among collegiate football players /." Birmingham, Ala. : University of Alabama at Birmingham, 2009. https://www.mhsl.uab.edu/dt/2009m/hill.pdf.

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Donnelly, Jilian K. "Understanding Educational Choice Processes of Retired Professional Hockey Players." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/260106.

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Educational Administration
Ed.D.
The purpose of this qualitative study was to investigate the processes and influences that underlie the choice of retired elite athletes to further their education and assume the adult learner role. In the current study, focus was applied specifically to professional ice hockey players who were in a period of retirement from active play. Elite athletes often retire at a time when most other professions are just beginning or reaching a level of stability. Research suggests that many retired elite athletes experience a difficult transition to an early retirement from athletics that is fraught with depression and unemployment. A select number of these elite athletes choose to further their educations after their careers have expired. The literature suggests that some of these retired elite athletes find educational programming a valuable coping strategy in the often traumatic post-athletic career adjustment. Unfortunately, this choice is made by only few athletes. It would be desirable to encourage more retired athletes to consider and engage in educational activities; however, currently, there is only very little knowledge on the processes underlying athletes' choice to participate, or not to participate, in education in their retirement years. Using a theoretical framework that includes adult learning theory and adult development theory, in conjunction with expectancy-value theory of motivation, the impact of individual characteristics and environmental opportunities on post-athletic career choices made by professional athletes may be better understood. The primary instrument for data collection was a personal interview with ten retired professional hockey players, conducted over a consecutive six-month period resulting in significant data. Utilizing the constant-comparative method for data analysis, common themes were identified as indicators of educational engagement: Informal Mentorship, Head Injury Related Retirement, and Pre-Transition Planning. In addition to these themes, the findings reflected an alternative adult developmental model possibly unique to professional hockey players. The findings of this study are valuable to the larger conversation regarding adult learners, adult development, and elite athlete career transition.
Temple University--Theses
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Llobet, Martí Bernat. "Analysis of the interactivity in a teaching and learning sequence with novice rugby players: the transfer of learning responsibility and control." Doctoral thesis, Universitat de Girona, 2016. http://hdl.handle.net/10803/399791.

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This thesis is a compilation of 3 articles, and the main focus of attention is the learning transfer mechanism. The first article explains the Rugby Attack Assessment instrument, a tool that assesses game performance of rugby union during a 5v5 small-sided game, taking into account simple actions and more complex tactical behaviours. The second paper explores the use of the Integrated Technique-Tactical Model used during the teaching and learning sequence, and reports the learning outcomes of this sequence. Results at a macro-level show no significant improvements. Results at a micro-level show an increase of some tactical behaviours frequencies. The third article analyses the interactivity among participants and the transfer of learning responsibility from the coach to the players. The units of analysis are the segments of interactivity. Results show that this process is linked to a slight decrease of segmentation, and mainly to the transfer of reflection from specific segments of discussion to reflections done during the guided practice
Aquesta tesi és una compilació de 3 articles, i l'objectiu principal és eñ mecanisme de traspàs de l'aprenentatge. El primer article explica el Rugby Attack Assessment Instrument, una eina que avalua el rendiment col·lectiuen el rugbi en una situació reduïda de 5x5, tenint en compte accions simples i comportaments tàctics més complexos. El segon article explica l'ús del Model Integrat Tècnic-Tàctic utilitzat durant la seqüència d'ensenyament i aprenentatge, i explica els resultats de l'aprenentatge d'aquesta seqüència. Els resultats en un nivell macro revelen que no hi ha millores significatives. Els resultats a nivell micro mostren un increment de la freqüència de determinats comportaments tàctics. El tercer article analitza la interactivitat entre els participants i el traspàs de la responsabilitat de l'aprenentatge de l'entrenador als jugadors. Les unitats d'anàlisi són els segments d'interactivitat. Els resultats mostren que aquest procés està lligat a un lleuger descens de la segmentació i principalment a un traspàs dels moments de reflexió des de segments específics de discussió cap a reflexions dutes a terme durant la pràctica guiada
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Millard, Lourens, and Ryan Raffan. "Impact of visual skills training on the visual ability of elite rugby players." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/12692.

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The aim of this study was to investigate the effect of visual skills training on the visual ability of elite rugby players. This study utilized a quantitative approach; and it employed a true experimental study design, in particular a pre-test-post-test randomized-group design. A total number of 26 participants (that met the specified criteria) were selected from the Eastern Province Rugby Academy squad. Those that met the criteria were then divided randomly into two groups (13 per group), which comprised the experimental and control groups, respectively. The control and experimental groups were tested on six visual skills, namely: accommodation, saccadic eye movements, speed of recognition, hand-eye co-ordination, visual memory and peripheral awareness. An optometric assessment was also conducted as a pre-test to ensure that the participant had “normal vision”, followed by a six week intervention (visual skills-training). The sessions were performed for thirty minutes three times per week for six weeks, followed by a post-test. Descriptive statistics were employed, both numerically, such as the mean standard deviation, and graphically by means of histograms. Inferential statistics were also used in the form of t-tests and Cohen’s d calculations to determine the statistical and practical significance of the mean score differences between the experimental and control groups. The pre- to post-test results indicated both statistical and practical significant differences in each of the visual skills tested among the experimental group; accommodation (hart near far rock test, p<.0005, d=2.34), saccadic eye movements (saccadic eye movement test, p<.0005, d=2.08), hand-eye coordination and peripheral awareness (accumulator test, p<.0005, d=2.20), speed of recognition (evasion test, p<.0005, d=1.14) and visual memory (flash program test, p<.0005, d=1.20). However, there were no statistically significant differences (p>.05) found when comparing the pre- and post-tests for the control group. It was concluded that the visual skills training program used had a significant positive impact on the visual performance of the Eastern Province rugby academy players.
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Books on the topic "Players' learning"

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1930-, Jones Elizabeth, ed. Master players: Learning from children at play. New York: Teachers College Press, 1997.

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Buchanan, John. Learning from legends: Australian cricket. Pyrmont: Fairfax Media Publications, 2009.

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Stout, Glenn. From hardships to championships: Learning life's lessons. Boston: Houghton Mifflin Harcourt, 2013.

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Bain, David. Learning the violin: Finger positions in nine scales : a violin learning and teachingaid for violin players everywhere. Burry Port: D. Bain, 1996.

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Serious players in the primary classroom: Empowering children through active learning experiences. New York: Teachers College Press, 1990.

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Serious players in the primary classroom: Empowering children through active learning experiences. 2nd ed. New York: Teachers College Press, 2000.

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Waitzkin, Josh. The art of learning: A journey in the pursuit of excellence. New York: Free Press, 2007.

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Trueba, David. Learning to lose: A novel. New York: Other Press, 2009.

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Learning to lose. New York: Other Press, 2010.

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Learning to lose. London: Portobello, 2011.

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Book chapters on the topic "Players' learning"

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McArdle, Felicity, Jennifer Sumsion, Sue Grieshaber, Kellie Bousfield, and Paul Shield. "Team Players." In Play: A Theory of Learning and Change, 35–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25549-1_4.

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Macdonald, Verna. "The Park Bench Players." In Working the Margins of Community-Based Adult Learning, 145–55. Rotterdam: SensePublishers, 2016. http://dx.doi.org/10.1007/978-94-6300-483-1_12.

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Decroos, Tom, and Jesse Davis. "Player Vectors: Characterizing Soccer Players’ Playing Style from Match Event Streams." In Machine Learning and Knowledge Discovery in Databases, 569–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46133-1_34.

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de Vries, Marjolein, and Pieter Spronck. "Deep Learning for Classifying Battlefield 4 Players." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 161–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49616-0_15.

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Yen, Shi-Jim, Yi-Ling Chen, and Hsin-I. Lin. "Scaffolding Learning for the Novice Players of Go." In Lecture Notes in Computer Science, 139–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35343-8_15.

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Domeshek, Eric, Sowmya Ramachandran, Randy Jensen, and Jeremy Ludwig. "Realistic and Relevant Role-Players for Experiential Learning." In Adaptive Instructional Systems, 55–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50788-6_5.

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Aleemulla Khan, P., N. Thirupathi Rao, and Debnath Bhattacharyya. "Prediction of Cricket Players Performance Using Machine Learning." In Smart Technologies in Data Science and Communication, 155–62. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2407-3_20.

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Inal, Yavuz, Turkan Karakus, and Kursat Cagiltay. "Designing Narratology-Based Educational Games with Non-players." In Technologies for E-Learning and Digital Entertainment, 528–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69736-7_56.

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Muazu Musa, Rabiu, Anwar P. P. Abdul Majeed, Muhammad Zuhaili Suhaimi, Mohd Azraai Mohd Razman, Mohamad Razali Abdullah, and Noor Azuan Abu Osman. "Anthropometric Variables in the Identification of High-performance Volleyball Players." In Machine Learning in Elite Volleyball, 33–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3192-4_5.

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Cowley, Benjamin Ultan, Darryl Charles, Gerit Pfuhl, and Anna-Mari Rusanen. "Artificial Intelligence in Education as a Rawlsian Massively Multiplayer Game: A Thought Experiment on AI Ethics." In AI in Learning: Designing the Future, 297–316. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09687-7_18.

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AbstractIn this chapter, we reflect on the deployment of artificial intelligence (AI) as a pedagogical and educational instrument and the challenges that arise to ensure transparency and fairness to staff and students . We describe a thought experiment: ‘simulation of AI in education as a massively multiplayer social online game’ (AIEd-MMOG). Here, all actors (humans, institutions, AI agents and algorithms) are required to conform to the definition of a player. Models of player behaviour that ‘understand’ the game space provide an application programming interface for typical algorithms, e.g. deep learning neural nets or reinforcement learning agents, to interact with humans and the game space. The definition of ‘player’ is a role designed to maximise protection and benefit for human players during interaction with AI. The concept of benefit maximisation is formally defined as a Rawlsian justice game, played within the AIEd-MMOG to facilitate transparency and trust of the algorithms involved, without requiring algorithm-specific technical solutions to, e.g. ‘peek inside the black box’. Our thought experiment for an AIEd-MMOG simulation suggests solutions for the well-known challenges of explainable AI and distributive justice.
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Conference papers on the topic "Players' learning"

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Huang, Chi-Wen, Cheng-Yu Fan, Yu-Ling Chi, Chia-Jung Wu, and Gwo-Dong Chen. "High-Interactive Gaming Learning: Bring Different Learning Benefits to Team Players and Individual Players." In 2012 IEEE 12th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2012. http://dx.doi.org/10.1109/icalt.2012.176.

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Liu, Guiliang, and Oliver Schulte. "Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/478.

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A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player performance depends on the game context. This paper proposes a new approach to capturing game context: we apply Deep Reinforcement Learning (DRL) to learn an action-value Q function from 3M play-by-play events in the National Hockey League (NHL). The neural network representation integrates both continuous context signals and game history, using a possession-based LSTM. The learned Q-function is used to value players' actions under different game contexts. To assess a player's overall performance, we introduce a novel Game Impact Metric (GIM) that aggregates the values of the player's actions. Empirical Evaluation shows GIM is consistent throughout a play season, and correlates highly with standard success measures and future salary.
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Kowalski, Jakub, and Andrzej Kisielewicz. "Testing general game players against a Simplified Boardgames player using temporal-difference learning." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257061.

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Yahyasoltani, Nasim, Manzur Farazi, and Priyanka Annapureddy. "Learning Performance Efficiency of College Basketball Players." In 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys). IEEE, 2021. http://dx.doi.org/10.1109/hpcc-dss-smartcity-dependsys53884.2021.00197.

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Ranieri, Maria, and Cristina Gaggioli. "STEREOTYPES, VIDEOGAMES AND AWARE PLAYERS IN HIGHER EDUCATION." In 12th International Conference on Education and New Learning Technologies. IATED, 2020. http://dx.doi.org/10.21125/edulearn.2020.1279.

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Alhajry, Majed, Faisal Alvi, and Moataz Ahmed. "TD(λ) and Q-learning based Ludo players." In 2012 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 2012. http://dx.doi.org/10.1109/cig.2012.6374142.

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Wang, Pengcheng, Jonathan Rowe, Wookhee Min, Bradford Mott, and James Lester. "Interactive Narrative Personalization with Deep Reinforcement Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/538.

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Data-driven techniques for interactive narrative generation are the subject of growing interest. Reinforcement learning (RL) offers significant potential for devising data-driven interactive narrative generators that tailor players’ story experiences by inducing policies from player interaction logs. A key open question in RL-based interactive narrative generation is how to model complex player interaction patterns to learn effective policies. In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model. Specifically, the framework involves training a set of Q-networks to control adaptable narrative event sequences with long short-term memory network-based simulated players. We investigate the deep RL framework’s performance with an educational interactive narrative, Crystal Island. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives.
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Hocquette, Céline. "Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?" In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/909.

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World-class human players have been outperformed in a number of complex two person games such as Go by Deep Reinforcement Learning systems GO. However, several drawbacks can be identified for these systems: 1) The data efficiency is unclear given they appear to require far more training games to achieve such performance than any human player might experience in a lifetime. 2) These systems are not easily interpretable as they provide limited explanation about how decisions are made. 3) These systems do not provide transferability of the learned strategies to other games. We study in this work how an explicit logical representation can overcome these limitations and introduce a new logical system called MIGO designed for learning two player game optimal strategies. It benefits from a strong inductive bias which provides the capability to learn efficiently from a few examples of games played. Additionally, MIGO's learned rules are relatively easy to comprehend, and are demonstrated to achieve significant transfer learning.
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Nosu, Kiyhoshi, Tomoya Kurokawa, Hiroto Horita, Yoshitarou Ohhazama, and Hiroki Takeda. "Real Time Emotion-Diagnosis of Video Game Players from their Facial Expressions and its Applications to Voice Feed-Backing to Game Players." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370512.

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Luo, Yudong, Oliver Schulte, and Pascal Poupart. "Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/464.

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A major task of sports analytics is to rank players based on the impact of their actions. Recent methods have applied reinforcement learning (RL) to assess the value of actions from a learned action value or Q-function. A fundamental challenge for estimating action values is that explicit reward signals (goals) are very sparse in many team sports, such as ice hockey and soccer. This paper combines Q-function learning with inverse reinforcement learning (IRL) to provide a novel player ranking method. We treat professional play as expert demonstrations for learning an implicit reward function. Our method alternates single-agent IRL to learn a reward function for multiple agents; we provide a theoretical justification for this procedure. Knowledge transfer is used to combine learned rewards and observed rewards from goals. Empirical evaluation, based on 4.5M play-by-play events in the National Hockey League (NHL), indicates that player ranking using the learned rewards achieves high correlations with standard success measures and temporal consistency throughout a season.
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Reports on the topic "Players' learning"

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Devereux, Stephen, and Anna Wolkenhauer. Agents, Coercive Learning, and Social Protection Policy Diffusion in Africa. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/ids.2021.068.

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This paper makes theoretical, empirical, and methodological contributions to the study of social policy diffusion, drawing on the case of social protection in Africa, and Zambia in particular. We examine a range of tactics deployed by transnational agencies (TAs) to encourage the adoption of cash transfers by African governments, at the intersection between learning and coercion, which we term ‘coercive learning’, to draw attention to the important role played by TA-commissioned policy drafting, evidence generation, advocacy, and capacity-building activities. Next, we argue for making individual agents central in the analysis of policy diffusion, because of their ability to reflect, learn, and interpret policy ideas. We substantiate this claim theoretically by drawing on practice theories, and empirically by telling the story of social protection policy diffusion in Zambia through three individual agents. This is complemented by two instances of self-reflexivity in which the authors draw on their personal engagements in the policy process in Zambia, to refine our conclusions about the interplay of structure and agency.
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Douglas, Thomas, and Caiyun Zhang. Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41222.

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The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.
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Kaffenberger, Michelle, Danielle Sobol, and Deborah Spindelman. The Role of Low Learning in Driving Dropout: A Longitudinal Mixed Methods Study in Four Countries. Research on Improving Systems of Education (RISE), April 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/070.

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Using unique longitudinal quantitative and qualitative data, we examine the role that low learning plays in driving dropout in Ethiopia, India, Peru, and Vietnam. Regression analysis using IRT-linked test scores and data on schooling attainment and dropout shows a strong, significant association with one standard deviation higher test scores associated with 50 percent lower odds of dropping out between the ages of 8 and 12, and a similar association between the ages of 12 and 15. Qualitative analysis indicates a direct relationship between low learning and dropout, with children and parents choosing to discontinue school when they realize how little is being learned. Qualitative findings also show that low learning interacts with and exacerbates more proximate causes of dropout, with low learning often contributing to choices of early marriage (for girls) and of leaving school to work (for both genders), with families making practical decisions about which options will best provide for children in the long run. Finally, learning, work, and poverty often interact, as the need to work to help provide for the household reduces the opportunities to learn, and low learning tilts the opportunity cost of time in favor of working. These findings suggest that low learning may play a larger role in dropout decisions, by underlying and interacting with other causes, than has been typically recognized.
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Ismailova, L. Yu, O. O. Zhuravleva, O. I. Bazhenova, V. S. Zaytsev, and I. O. Sleptsov. educational computer game "family meeting" (version 1.0). SIB-Expertise, July 2022. http://dx.doi.org/10.12731/er0578.04072022.

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COMPUTER LEARNING GAME DESIGNED TO STUDY FAMILY LAW. THE GAME ALLOWS IN AN INTERACTIVE MODE TO TEST YOUR STRENGTH IN SOLVING A LARGE NUMBER OF THEORETICAL AND PRACTICAL QUESTIONS. THE STUDENT CAN WORK OUT NEW TOPICS BY USING THE GAME’S EXPLANATIONS AND REFERENCES TO NORMATIVE ACTS SO CHECK YOUR UPTAKE. THE GAME CHARACTERS AND THEIR EXPRESSIONS MOTIVATE THE PLAYER TO CAREFULLY WORK WITH THE OBJECT AND THE OBJECT OF THE GAME AND TO WORK ON THESE TOPICS INDEPENDENTLY. THE CONTENT OF THE GAME IS IN ACCORDANCE WITH THE STATE STANDARD PROGRAM OF "JURISPRUDENCE". THE MAIN OBJECTIVE OF THE GAME. THE GAME "THE MEETING" CAN BE USEFUL FOR LAW STUDENTS AND FACULTIES, PRACTISING LAWYERS AND ANYONE WISHING TO IMPROVE THEIR QUALIFICATIONS IN THE FIELD OF FAMILY LAW.
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Yorke, Lynda, Simon Tate, and Martin Davis. New to teaching geography. Royal Geographical Society (with IBG), December 2021. http://dx.doi.org/10.55203/gvkz5128.

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Geography plays a crucial role in understanding our world. It makes a vital contribution to our knowledge of the rapidly changing environmental and social challenges facing us and how we should tackle them. Supporting them effectively can present multiple opportunities and challenges for academic staff. For those embarking on a career in geographical teaching and learning, recognising these issues, and developing a toolbox of strategies with which to address them is key to developing good teaching practice. This guide offers a starting point for this process for graduate teaching assistants, teaching fellows and demonstrators.
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Freed, Danielle. K4D Strengthens Partners’ Ability to Deliver Improved Results for Inclusion in Crises. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.161.

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Achieving Sustainable Development Goals requires the inclusion of the most vulnerable people affected by intersecting inequalities. Inclusion is an approach and objective that serves to counter structural discrimination and enable affected people and communities to withstand the causes and impacts of crises. Recognising the need to address this issue, the UK government has sought to implement a range of commitments relating to inclusion in its programme and policy responses to crises. The K4D Inclusion in Crises Learning Journey has played a key role in supporting reflection on the opportunities and challenges for operationalising these commitments and equipping participants with the tools needed to make sure programmes can deliver high impact results, improving the lives and wellbeing of people who are marginalised and crisis-affected.
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Babu M.G., Sarath, Debjani Ghosh, Jaideep Gupte, Md Asif Raza, Eric Kasper, and Priyanka Mehra. Kerala’s Grass-roots-led Pandemic Response: Deciphering the Strength of Decentralisation. Institute of Development Studies (IDS), June 2021. http://dx.doi.org/10.19088/ids.2021.049.

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This paper presents an analysis of the role of decentralised institutions to understand the learning and challenges of the grass-roots-led pandemic response of Kerala. The study is based on interviews with experts and frontline workers to ensure the representation of all stakeholders dealing with the outbreak, from the state level to the household level, and a review of published government orders, health guidelines, and news articles. The outcome of the study shows that along with the decentralised system of governance, the strong grass-roots-level network of Accredited Social Health Activists (ASHA) workers, volunteer groups, and Kudumbashree members played a pivotal role in pandemic management in the state. The efficient functioning of local bodies in the state, experience gained from successive disasters, and the Nipah outbreak naturally aided grass-roots-level actions. The lessons others can draw from Kerala are the importance of public expenditure on health, investment for building social capital, and developing the local self-delivery system.
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Fedorenko, Elena G., Nataliia V. Kaidan, Vladyslav Ye Velychko, and Vladimir N. Soloviev. Gamification when studying logical operators on the Minecraft EDU platform. [б. в.], July 2021. http://dx.doi.org/10.31812/123456789/4624.

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Use of visual methods plays a significant role in learning. ICT allow us to create electronic educational resources in a new format and with new opportunities. The study of their didactic possibilities, forms and methods of their application is a topical issue. Simulation, virtualization, gamification requires new knowledge about their application, and therefore, the problem of training future teachers to use them is an urgent and important part of training. In this article modern achievements in the use of serious games in education were investigated and analyzed, the possibilities of using virtual worlds in education were considered, the recommendations for the practical training of future teachers to use them were developed. In practice, the effectiveness of the use of virtual tools in education has been tested. A pedagogical experiment has been launched to identify the effectiveness of gamification in the realities of education in Ukraine.
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Avellán, Leopoldo, Zulima Leal Calderon, and Giulia Lotti. Why do some Development Projects Disburse Funds Faster than Others. Inter-American Development Bank, November 2021. http://dx.doi.org/10.18235/0003839.

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The timely disbursement of funds is a necessary condition for the success of international development projects. Disbursements track the progress of projects in completing the products that ultimately will deliver the projects desired outcomes. Moreover, in a world with pressing needs for external financing, project disbursements are an important source of external liquidity for recipient countries. However, some projects start disbursing faster than others and at relatively larger amounts. Hence learning why some projects disburse faster than others is important to understand not only which projects are more likely to achieve development outcomes sooner, but also to assess their value as providers of external liquidity in times of distress. As it has become evident over the past year with the COVID-19 pandemic, multilateral lending has played a crucial role in helping emerging countries face the larger financing needs originated by the crisis. In 2020, Inter-American Development Bank (IDB) disbursements increased 49% over 2019, reaching $13.4 billion, more than doubling the baseline disbursement projection. This paper assesses which observable characteristics of investment loans offered by the IDB are associated with faster disbursements. The results indicate substantial heterogeneity across countries, sectors, and loan modalities. All else constant, results-based loans and loans in the social sector are more likely to disburse within 2 years after being approved. Projects in countries where it takes longer to meet at least some of the clauses to start disbursing are less likely to start disbursing 2 years after approval. Projects that are expected to have longer execution times disburse at slower speeds within 24 months after approval. Overall, country factors seem to play a more relevant role than sectorial factors in explaining the probability that a project will disburse funds quickly. These and other findings in the paper can inform future programming exercises and help optimize the disbursement processes.
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Roschelle, Jeremy, Britte Haugan Cheng, Nicola Hodkowski, Julie Neisler, and Lina Haldar. Evaluation of an Online Tutoring Program in Elementary Mathematics. Digital Promise, April 2020. http://dx.doi.org/10.51388/20.500.12265/94.

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Many students struggle with mathematics in late elementary school, particularly on the topic of fractions. In a best evidence syntheses of research on increasing achievement in elementary school mathematics, Pelligrini et al. (2018) highlighted tutoring as a way to help students. Online tutoring is attractive because costs may be lower and logistics easier than with face-to-face tutoring. Cignition developed an approach that combines online 1:1 tutoring with a fractions game, called FogStone Isle. The game provides students with additional learning opportunities and provides tutors with information that they can use to plan tutoring sessions. A randomized controlled trial investigated the research question: Do students who participate in online tutoring and a related mathematical game learn more about fractions than students who only have access to the game? Participants were 144 students from four schools, all serving low-income students with low prior mathematics achievement. In the Treatment condition, students received 20-25 minute tutoring sessions twice per week for an average of 18 sessions and also played the FogStone Isle game. In the Control condition, students had access to the game, but did not play it often. Control students did not receive tutoring. Students were randomly assigned to condition after being matched on pre-test scores. The same diagnostic assessment was used as a pre-test and as a post-test. The planned analysis looked for differences in gain scores ( post-test minus pre-test scores) between conditions. We conducted a t-test on the aggregate gain scores, comparing conditions; the results were statistically significant (t = 4.0545, df = 132.66, p-value < .001). To determine an effect size, we treated each site as a study in a meta-analysis. Using gain scores, the effect size was g=+.66. A more sophisticated treatment of the pooled standard deviation resulted in a corrected effect size of g=.46 with a 95% confidence interval of [+.23,+.70]. Students who received online tutoring and played the related Fog Stone Isle game learned more; our research found the approach to be efficacious. The Pelligrini et al. (2018) meta-analysis of elementary math tutoring programs found g = .26 and was based largely on face-to-face tutoring studies. Thus, this study compares favorably to prior research on face-to-face mathematics tutoring with elementary students. Limitations are discussed; in particular, this is an initial study of an intervention under development. Effects could increase or decrease as development continues and the program scales. Although this study was planned long before the current pandemic, results are particularly timely now that many students are at home under shelter-in-place orders due to COVID-19. The approach taken here is feasible for students at home, with tutors supporting them from a distance. It is also feasible in many other situations where equity could be addressed directly by supporting students via online tutors.
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