Due to the popularity of the Computer science field many students study programming. With large numbers of student enrollments in undergraduate courses, assessing programming submissions is becoming an increasingly tedious task that requires high cognitive load, and considerable amount of time and effort. Programming assignments usually contain algorithmic implementations written in specific programming languages to assess students' logical thinking and problem-solving skills. Evaluators use either a test case-driven or source code analysis approach when evaluating programming assignments. Given that many marking rubrics and evaluation criteria provide partial marks for programs that are not syntactically correct, evaluators are required to analyze the source code during evaluations. This extra step adds additional burden on evaluators that consumes more time and effort. Hence, this research work attempts to study existing automatic source code analysis mechanisms, specifically, use of deep learning approaches in the domain of automatic assessments. Such knowledge may lead to creating novel automated marking models using past student data and apply deep learning techniques to implement automatic assessments of programming assignments irrespective of the computer language or the algorithm implemented.

We study the performance and user experience of two popular mainstream mobile text entry methods: the Smart Touch Keyboard (STK) and the Smart Gesture Keyboard (SGK). Our first study is a lab-based ten-session text entry experiment. In our second study we use a new text entry evaluation methodology based on the experience sampling method (ESM). In the ESM study, participants installed an Android app on their own mobile phones that periodically sampled their text entry performance and user experience amid their everyday activities for four weeks. The studies show that text can be entered at an average speed of 28 to 39 WPM, depending on the method and the user's experience, with 1.0% to 3.6% character error rates remaining. Error rates of touchscreen input, particularly with SGK, are a major challenge; and reducing out-of-vocabulary errors is particularly important. Both SGK and STK have strengths, weaknesses, and different individual awareness and preferences. Two-thumb touch typing in a focused setting is particularly effective on STK, whereas one-handed SGK typing with the thumb is particularly effective in more mobile situations. When exposed to both, users tend to migrate from STK to SGK. We also conclude that studies in the lab and in the wild can both be informative to reveal different aspects of keyboard experience, but used in conjunction is more reliable in comprehensively assessing input technologies of current and future generations.

We present VelociTap: a state-of-the-art touchscreen keyboard decoder that supports a sentence-based text entry approach. VelociTap enables users to seamlessly choose from three word-delimiter actions: pushing a space key, swiping to the right, or simply omitting the space key and letting the decoder infer spaces automatically. We demonstrate that VelociTap has a significantly lower error rate than Google's keyboard while retaining the same entry rate. We show that intermediate visual feedback does not significantly affect entry or error rates and we find that using the space key results in the most accurate results. We also demonstrate that enabling flexible word-delimiter options does not incur an error rate penalty. Finally, we investigate how small we can make the keyboard when using VelociTap. We show that novice users can reach a mean entry rate of 41 wpm on a 40 mm wide smartwatch-sized keyboard at a 3% character error rate.