The aim of this thesis was to investigate how students, teachers and special teachers use, or may want to use, mobile phones as a resource in everyday schoolwork. An analysis of the result was made base on two main theories. One i Säljös theory about the sociocultural perspective, where artefacts play an important part in the devolopment of human society. The other is Laurillardss thougts of social learning and the use of technology in teaching. The methods used were inquiries and semi-structured interwiews with individuals and groups. The result shows that students in compulsory school use the mobile phone as a calculator and för listening to music as a means to concentrate. Senior high school students use the mobile phone as a calculator, to seek information on the internet, and to communicate aboute schoolwork. The older the students, the more they appreciated the use of the mobile phones calendar and its reminder function. Both students, teachers and special teachers could see benefits in using the mobile phone in schoolwork, for example using apps, making recordings and reading e-books. Older students, and some teachers, wanted to use the mobile phone as a means for accessing shared knowledge.

The iPad and other mobile devices have become so popular over the past few years that many school districts are purchasing these devices and implementing them in the classroom with little to no research. Because there has been no previous research at one rural school district in Michigan, the primary purpose of this quantitative causal-comparative study was to investigate the effects that a 1-to-1 iPad initiative program has had on only 11th grade student achievement and determine if 11th grade students' test scores on the Michigan Merit Exam in the areas of mathematics, science, and social studies for each school year from 2007 to 2016 have improved, declined, or stayed the same. The framework for this study was rooted in Kearsley and Shneiderman's engagement theory, which specifically applies to technology-based learning environments. A repeated measures analysis of variance was used to compare the standardized test scores from 2007 to 2016, with the scores as the dependent variables and the introduction of the iPad technology as the independent variable. Student characteristics of gender, ethnicity, and socioeconomic status were covariates. The findings from this study indicated that the iPad has improved standardized test scores at this local high school and therefore this school district should continue the promotion and investment in mobile learning devices and other technologies. The resulting policy recommendation from this study prompts the local school district to pursue the expansion of a 1-to-1 iPad program or other mobile learning device in the current curriculum to help increase student achievement on standardized tests. The incorporation of Apple's iPad in the classroom has potentially created a solution to help students increase academic performance and achieve higher levels on standardized and state tests.

The name ”smart homes” gives a promise of intelligent behavior. Today automation of the home environment is a manual task, with the creation of rules controlling devices relying on the user. For smart homes this tedious manual task can be automated. The purpose of this thesis is development of a prototype that will help users in smart homes create rules. The rules should be automatically created by the use of a machine learning solution. A learning classifier system algorithm is found as a suitable machine learning solution. A learning classifier system is used to find and create rules from sensor data. In the prototype a Raspberry Pi is used to collect the data. This data is processedby the learning classifier system, generating a set of rules. These rules predict actions for controlling a smart lighting system. The rules are continuously updated with new sensory information from the environment constantly reevaluating the previous found rules. The learning classifier system prototype solves the problem of how rules can be generated automatically by the use of machine learning. / Uttrycket ”smarta hem” utlovar ett intelligent beteende. Idag är automatiseringen av hemmiljön en manuell uppgift, där användaren formulerar regler som styr systemet. I smarta hem kan denna uppgift bli automatiserad. Syftet med denna kandidatuppsats är att utveckla en prototyp som ska hjälpa användare i smarta hem att skapa regler. Reglerna ska skapas automatiskt med hjälp av en maskininlärningslösning. Ett självlärande klassificeringssystem bedöms uppfylla den kravställning som görs. Det självlärande klassificeringssystemet används för att skapa regler från sensordata. I prototypen används en Raspberry Pi för att samla in data. Insamlad data behandlas av det självlärande klassificeringssystem som genererar en uppsättning regler. Dessa regler används för att kontrollera ett smart ljussystem. Reglerna uppdateras kontinuerligt med ny sensorinformation från omgivningen och utvärderar de tidigare funna reglerna. Den självlärande klassificeringssystemprototypen löser problemet om hur regler kan skapas automatiskt med hjälp av maskininlärning.

Tato práce pojednává o vytvoření mobilní aplikace zvažující kontext zařízení, která vybírá a doporučuje hudební skladby dle aktuálního stavu kontextu zařízení. Kontext je získáván na základě naměřených hodnot, které jsou získány z vestavěných senzorů mobilního zařízení a z ostatních systémových hodnot zařízení. Výběr konkrétní skladby je poté založen na výstupu modelu strojového učení, který klasifikuje kontext na základě aktuálních získaných dat a následně zvolí skladbu připadající k danému kontextu.

Táto práca sa zaoberá problematikou identifikácie sieťových zariadení na základe ich chovania v sieti. S neustále sa zvyšujúcim počtom zariadení na sieti je neustále dôležitejšia schopnosť identifikovať zariadenia z bezpečnostných dôvodov. Táto práca ďalej pojednáva o základoch počítačových sietí a metódach, ktoré boli využívané v minulosti na identifikáciu sieťových zariadení. Následne sú popísané algoritmy využívané v strojovom učení a taktiež sú popísané ich výhody i nevýhody. Nakoniec, táto práca otestuje dva tradičné algorithmy strojového učenia a navrhuje dva nové prístupy na identifikáciu sieťových zariadení. Výsledný navrhovaný algoritmus v tejto práci dosahuje 89% presnosť identifikácii sieťových zariadení na reálnej dátovej sade s viac ako 10000 zariadeniami.