v anglickém jazyce Title: How the students of the 9th grades of elementary schools understand the digital versions of texts comparing to the printed ones. Goal: The dissertation aims at drawing the comparaison between the digital and the printed versions of the identical materials. The research was held in a number of selected schools. The materials were presented via personal computers, tablet personal computers and in printed versions. Our goal is to determine the differences among all the three kinds of media in terms of understanding the presented texts. Procedure: The essay is primarily based on the historical and the present days research of comprehensive reading. It also follows the bachelor essay written in 2014. Output: The theoretical processing of basic terms and the review of both the previous and the latest outcomes of the scientific literature. The most important part of the essay were focus on the research and the subsequent assessment of the findings. The research itself was preceded by a pre - research. The essay included the qualitative research which was reached through one of the comprehension test methods. The students were provided with the questionnaire comprising questions. The text was distributed both in the digital and in the printed versions.

I denna kunskapsöversikt har vi författare haft som syfte att fördjupa oss inom filmundervisning i skolan för att undersöka hur film kan användas i undervisning. Vi vill även undersöka om det finns forskning kring om film som undervisningsmaterial skulle kunna medföra problematik i undervisningen. För att undersöka detta har vi använt oss av både nationell och internationell forskning. Vi har gjort sökningar av vetenskapliga artiklar i databaserna ERIC, Libsearch och SwePub, men även i relevant litteratur. Forskning visar att filmundervisning i skolan kan hjälpa elevers avkodning och skrivprocess. Vidare menar den forskning som tas upp i den här kunskapsöversikten att film inte bara skapar engagemang och motivation bland eleverna i vid arbete med detta i klassrummet. Forskning tar upp att det kan finnas kopplingar mellan arbete med film och förbättring av elevers tal- och lyssningsfärdgheter. Samtidigt visar forskning på att det finns en viss okunskap kring hur film används som didaktiskt verktyg. Kunskapsöversikten visar att även om filmmediet har en didaktisk potential kan denna form av undervisning bära med sig vissa utmaningar.

Magister Commercii (Information Management) - MCom(IM) / South Africa has one of the largest HIV and AIDS burdens in the world, with an estimated7.52 million people living with HIV in 2018. The antiretroviral therapy (ART) programme is the biggest and most costly programme in the country, with 3.7 million people enrolled as of 2017. The success of antiretroviral therapy is dependent on adherence to medication and long-term retention in care. It has been reported that support groups can improve the treatment adherence of patients and their retention in care. However, enrolment in adherence support groups is voluntary, and the abovementioned success thereof is dependent on the commitment of the patient to active participation in the group. It is estimated that about 80% of adults and young people own at least one mobile phone, which makes this technology suitable to improve communication and enhance interaction amongst support group members.

Text classification is a subset of machine learning which is used to classify texts such as tweets, email, news headlines or articles, with tags or categories. As news publishing can have uncertainty in their categorisations, text classification could categorise articles autonomously and distinguish unclear categorisations. The library TensorFlow helps with operations and tools for the machine learning workflow.  This paper takes focus on the crucial parts of working with machine learning using TensorFlow.js and to what extent this model can categorise a news article. The authors evaluates different models to analyse how optimising the settings will affect the accuracy of the model. Results of this paper was researched with a literature study of official documentations and peer reviewed reports. An empirical experiment where machine learning models were trained in TensorFlow.js was also performed. The results showed that the model with the highest accuracy with 87.17% accuracy was trained with 1000 articles using Relu and Softmax activation functions and the Mean squared error loss function. While the model with lowest accuracy had 75.5% using Sigmoid activation functions and Categorical cross-entropy on the 5000 articles training set. Crucial parts for this development were: optimizer function, loss function, batch size, activation functions, training data and test data with labels, normalise function, shapes of layers and computing power. There are several parts and functions to take in consideration when developing a machine learning model with text classification in TensorFlow.js. The training process needs to be performed multiple times as there are many parameters which has an affect on the model results. The model results can be improved by optimising and finding the best combination between different functions and parameters.

This work examines the word yea in the Book of Mormon, the Earliest Text and enumerates the usages found therein. Already recognized definitions in addition to new definitions are given with examples. Also included are textual variations from the Earliest Text and the current Book of Mormon used generally as scripture in the Church of Jesus Christ of Latter Day Saints.

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Text analysis is a significant branch of natural language processing, and includes manydifferent sub-fields such as topic modeling, document classification, and sentiment analysis.Unsurprisingly, those who do text analysis are concerned with the runtime of their algorithmsSome of these algorithms have runtimes that depend jointly on the size of the corpus beinganalyzed, as well as the size of that corpus's vocabulary. Trivially, a user may reduce theamount of data they feed into their model to speed it up, but we assume that users will behesitant to do this as more data tends to lead to better model quality. On the other hand,when the runtime also depends on the vocabulary of the corpus, a user may instead modifythe vocabulary to attain a faster runtime. Because elements of the vocabulary also add tomodel quality, this puts users into the position of needing to modify the corpus vocabulary inorder to reduce the runtime of their algorithm while maintaining model quality. To this end,we look at the relationship between model quality and runtime for text analysis by looking atthe effect that current techniques in vocabulary reduction have on algorithmic runtime andcomparing that with their effect on model quality. Despite the fact that this is an importantrelationship to investigate, it appears little work has been done in this area. We find thatmost preprocessing methods do not have much of an effect on more modern algorithms, butproper rare word filtering gives the best results in the form of significant runtime reductionstogether with slight improvements in accuracy and a vocabulary size that scales efficiently aswe increase the size of the data.

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