Den för datalogin grundläggande datastrukturen hashtabell är krävande att tillägna sig. En undersökning gjordes på en kurs i algoritmer och datastrukturer med 200 deltagare. Lärandemålet ”implementera hashtabell och hashfunktion” bröts ner i grundläggande färdigheter som testades i en automaträttad programmeringsuppgift med riktad återkoppling till studenterna. 86 studenter gjorde 334 försök att lösa uppgiften. Undersökningen visade att testerna som ingår i den automaträttade uppgiften svarar mot de fel studenterna gör. Studenternas fel kategoriserades efter de grundläggande färdigheter som tagit fram. Kategoriseringen kan användas för att identifiera svaga områden hos studenterna och modifiera undervisningen därefter. Försöken visar också att när uppgiften kräver samtidig tillämpning av två begrepp leder detta ofta till fel i implementationen av algoritmen eller ineffektiva lösningar. / The hash table data structure, which is fundamental to computer science, is demanding to learn. A survey was conducted in a course on algorithms and data structures with 200 participants. The learning outcome of implementing a hash table was broken down into basic skills that were tested in an automated programming task with targeted feedback to the students. 86 students made 334 attempts to solve the task. The study showed that the tests included in the automated task correspond to the errors made by the students. The students' errors were categorized according to the basic skills developed. The categorisation can be used to identify weak areas in the students and modify the teaching accordingly. The experiments also show that when the task requires the simultaneous application of two concepts, this often leads to errors in the implementation of the algorithm or inefficient solutions.

In an increasingly interconnected world where English has become the lingua franca of business, culture, entertainment, and academia, learners of English as a second language (L2) have been steadily growing. This has contributed to an increasing demand for automatic spoken language assessment systems for formal settings and practice situations in Computer-Assisted Language Learning. One common misunderstanding about automated assessment is the assumption that machines should replicate the human process of assessment. Instead, computers are programmed to identify, extract, and quantify features in learners' productions, which are subsequently combined and weighted in a multidimensional space to predict a proficiency level or grade. In this regard, transferring human assessment knowledge and skills into an automatic system is a challenging task since this operation should take into account the complexity and the specificities of the proficiency construct. This PhD thesis presents research conducted on methods and techniques for the automatic assessment and feedback of L2 spoken English, mainly focusing on the application of deep learning approaches. In addition to overall proficiency grades, the main forms of feedback explored in this thesis are feedback on grammatical accuracy and assessment related to particular aspects of proficiency (e.g., grammar, pronunciation, rhythm, fluency, etc.). The first study explores the use of written data and the impact of features extracted through grammatical error detection on proficiency assessment, while the second illustrates a pipeline which starts from disfluency detection and removal, passes through grammatical error correction, and ends with proficiency assessment. Grammar, as well as rhythm, pronunciation, and lexical and semantic aspects, is also considered in the third study, which investigates whether it is possible to use systems targeting specific facets of proficiency analytically when only holistic scores are available. Finally, in the last two studies, we investigate the use of self-supervised learning speech representations for both holistic and analytic proficiency assessment. While aiming at enhancing the performance of state-of-the-art automatic systems, the present work pays particular attention to the validity and interpretability of assessment both holistically and analytically and intends to pave the way to a more profound and insightful knowledge and understanding of automatic systems for speaking assessment and feedback.