Once and for all - How people change strategy to ignore irrelevant information in visual tasks

Ignoring irrelevant visual information aids efficient interaction with task environments. We studied how people, after practice, start to ignore the irrelevant aspects of stimuli. For this we focused on how information reduction transfers to rarely practised and novel stimuli. In Experiment 1, we compared competing mathematical models on how people cease to fixate on irrelevant parts of stimuli. Information reduction occurred at the same rate for frequent, infrequent, and novel stimuli. Once acquired with some stimuli, it was applied to all. In Experiment 2, simplification of task processing also occurred in a once-for-all manner when spatial regularities were ruled out so that people could not rely on learning which screen position is irrelevant. Apparently, changes in eye movements were an effect of a once-for-all strategy change rather than a cause of it. Overall, the results suggest that participants incidentally acquired knowledge about regularities in the task material and then decided to voluntarily apply it for efficient task processing. Such decisions should be incorporated into accounts of information reduction and other theories of strategy change in skill acquisition.

Time patterns and perceptions of online learning success factors

Online learning provides the opportunity to work on academic tasks at any time at the same time as doing other activities, such as using in web 2.0 tools. This study identifies factors that contribute to success in online learning from the students¿ perspective and their relationship with time patterns. A survey of learning outputs was used to find relationships between students¿ satisfaction, knowledge acquisition and knowledge transfer with time for working on academic tasks. In this study, 199 students from a university in Mexico completed the survey. Findings suggest that knowledge transfer has a significant association with the number of hours online per day, hours spent on social networks and the use made of e-learning during working hours. Learner satisfaction has a strong relationship with the time in years a learner has been using the Internet and the number of hours devoted to the course per week. The findings of this research will be helpful for faculty and instructional designers for implementing learning strategies.

Task complexity, the Cognition Hypothesis and second language learning and performance

In this paper we describe a taxonomy of task demands which distinguishes between Task Complexity, Task Condition and Task Difficulty. We then describe three theoretical claims and predictions of the Cognition Hypothesis (Robinson 2001, 2003b, 2005a) concerning the effects of task complexity on: (a) language production; (b) interaction and uptake of information available in the input to tasks; and (c) individual differences-task interactions. Finally we summarize the findings of the empirical studies in this special issue which all address one or more of these predictions and point to some directions for continuing, future research into the effects of task complexity on learning and performance.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Language Learning in Task Management and Task Accomplishment

In this paper we study student interaction in English and Swedish courses at a Finnish university. We focus on language choices made in task-related activities in small group interaction. Our research interests arose from the change in the teaching curriculum, in which content and language courses were integrated at Tampere University of Technology in 2013. Using conversation analysis, we analysed groups of 4-5 students who worked collaboratively on a task via a video conference programme. The results show how language alternation has different functions in 1) situations where students orient to managing the task, e.g., in transitions into task, or where they orient to technical problems, and 2) situations where students accomplish the task. With the results, we aim to show how language alternation can provide interactional opportunities for language learning. The findings will be useful in designing tasks in the future.

Learning Robot Motions from Visual Sensing

Learning from demonstration becomes increasingly popular as an efficient way of robot programming. Not only a scientific interest acts as an inspiration in this case but also the possibility of producing the machines that would find application in different areas of life: robots helping with daily routine at home, high performance automata in industries or friendly toys for children. One way to teach a robot to fulfill complex tasks is to start with simple training exercises, combining them to form more difficult behavior. The objective of the Master’s thesis work was to study robot programming with visual input. Dynamic movement primitives (DMPs) were chosen as a tool for motion learning and generation. Assuming a movement to be a spring system influenced by an external force, making this system move, DMPs represent the motion as a set of non-linear differential equations. During the experiments the properties of DMP, such as temporal and spacial invariance, were examined. The effect of the DMP parameters, including spring coefficient, damping factor, temporal scaling, on the trajectory generated were studied.

Verbal learning in mild cognitive impairment and alzheimer's disease : behavioural and neural approaches

The main focus of the present thesis was at verbal episodic memory processes that are particularly vulnerable to preclinical and clinical Alzheimer’s disease (AD). Here these processes were studied by a word learning paradigm, cutting across the domains of memory and language learning studies. Moreover, the differentiation between normal aging, mild cognitive impairment (MCI) and AD was studied by the cognitive screening test CERAD. In study I, the aim was to examine how patients with amnestic MCI differ from healthy controls in the different CERAD subtests. Also, the sensitivity and specificity of the CERAD screening test to MCI and AD was examined, as previous studies on the sensitivity and specificity of the CERAD have not included MCI patients. The results indicated that MCI is characterized by an encoding deficit, as shown by the overall worse performance on the CERAD Wordlist learning test compared with controls. As a screening test, CERAD was not very sensitive to MCI. In study II, verbal learning and forgetting in amnestic MCI, AD and healthy elderly controls was investigated with an experimental word learning paradigm, where names of 40 unfamiliar objects (mainly archaic tools) were trained with or without semantic support. The object names were trained during a 4-day long period and a follow-up was conducted one week, 4 weeks and 8 weeks after the training period. Manipulation of semantic support was included in the paradigm because it was hypothesized that semantic support might have some beneficial effects in the present learning task especially for the MCI group, as semantic memory is quite well preserved in MCI in contrast to episodic memory. We found that word learning was significantly impaired in MCI and AD patients, whereas forgetting patterns were similar across groups. Semantic support showed a beneficial effect on object name retrieval in the MCI group 8 weeks after training, indicating that the MCI patients’ preserved semantic memory abilities compensated for their impaired episodic memory. The MCI group performed equally well as the controls in the tasks tapping incidental learning and recognition memory, whereas the AD group showed impairment. Both the MCI and the AD group benefited less from phonological cueing than the controls. Our findings indicate that acquisition is compromised in both MCI and AD, whereas long13 term retention is not affected to the same extent. Incidental learning and recognition memory seem to be well preserved in MCI. In studies III and IV, the neural correlates of naming newly learned objects were examined in healthy elderly subjects and in amnestic MCI patients by means of positron emission tomography (PET) right after the training period. The naming of newly learned objects by healthy elderly subjects recruited a left-lateralized network, including frontotemporal regions and the cerebellum, which was more extensive than the one related to the naming of familiar objects (study III). Semantic support showed no effects on the PET results for the healthy subjects. The observed activation increases may reflect lexicalsemantic and lexical-phonological retrieval, as well as more general associative memory mechanisms. In study IV, compared to the controls, the MCI patients showed increased anterior cingulate activation when naming newly learned objects that had been learned without semantic support. This suggests a recruitment of additional executive and attentional resources in the MCI group.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Playing to Learn: Business Simulation Games as Leadership Learning Environments

The aim of this dissertation is to investigate if participation in business simulation gaming sessions can make different leadership styles visible and provide students with experiences beneficial for the development of leadership skills. Particularly, the focus is to describe the development of leadership styles when leading virtual teams in computer-­supported collaborative game settings and to identify the outcomes of using computer simulation games as leadership training tools. To answer to the objectives of the study, three empirical experiments were conducted to explore if participation in business simulation gaming sessions (Study I and II), which integrate face-­to-­face and virtual communication (Study III and IV), can make different leadership styles visible and provide students with experiences beneficial for the development of leadership skills. In the first experiment, a group of multicultural graduate business students (N=41) participated in gaming sessions with a computerized business simulation game (Study III). In the second experiment, a group of graduate students (N=9) participated in the training with a ‘real estate’ computer game (Study I and II). In the third experiment, a business simulation gaming session was organized for graduate students group (N=26) and the participants played the simulation game in virtual teams, which were organizationally and geographically dispersed but connected via technology (Study IV). Each team in all experiments had three to four students and students were between 22 and 25 years old. The business computer games used for the empirical experiments presented an enormous number of complex operations in which a team leader needed to make the final decisions involved in leading the team to win the game. These gaming environments were interactive;; participants interacted by solving the given tasks in the game. Thus, strategy and appropriate leadership were needed to be successful. The training was competition-­based and required implementation of leadership skills. The data of these studies consist of observations, participants’ reflective essays written after the gaming sessions, pre-­ and post-­tests questionnaires and participants’ answers to open-­ ended questions. Participants’ interactions and collaboration were observed when they played the computer games. The transcripts of notes from observations and students dialogs were coded in terms of transactional, transformational, heroic and post-­heroic leadership styles. For the data analysis of the transcribed notes from observations, content analysis and discourse analysis was implemented. The Multifactor Leadership Questionnaire (MLQ) was also utilized in the study to measure transformational and transactional leadership styles;; in addition, quantitative (one-­way repeated measures ANOVA) and qualitative data analyses have been performed. The results of this study indicate that in the business simulation gaming environment, certain leadership characteristics emerged spontaneously. Experiences about leadership varied between the teams and were dependent on the role individual students had in their team. These four studies showed that simulation gaming environment has the potential to be used in higher education to exercise the leadership styles relevant in real-­world work contexts. Further, the study indicated that given debriefing sessions, the simulation game context has much potential to benefit learning. The participants who showed interest in leadership roles were given the opportunity of developing leadership skills in practice. The study also provides evidence of unpredictable situations that participants can experience and learn from during the gaming sessions. The study illustrates the complex nature of experiences from the gaming environments and the need for the team leader and role divisions during the gaming sessions. It could be concluded that the experience of simulation game training illustrated the complexity of real life situations and provided participants with the challenges of virtual leadership experiences and the difficulties of using leadership styles in practice. As a result, the study offers playing computer simulation games in small teams as one way to exercise leadership styles in practice.

Integrating Student Learning into the Repository Ecosystem

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Employees’ continuous learning and job satisfaction - effects on productivity

In today’s knowledge intense economy the human capital is a source for competitive advantage for organizations. Continuous learning and sharing the knowledge within the organization are important to enhance and utilize this human capital in order to maximize the productivity. The new generation with different views and expectations of work is coming to work life giving its own characteristics on learning and sharing. Work should offer satisfaction so that the new generation employees would commit to organizations. At the same time organizations have to be able to focus on productivity to survive in the competitive market. The objective of this thesis is to construct a theory based framework of productivity, continuous learning and job satisfaction and further examine this framework and its applications in a global organization operating in process industry. Suggestions for future actions are presented for this case organization. The research is a qualitative case study and the empiric material was gathered by personal interviews concluding 15 employee and one supervisor interview. Results showed that more face to face interaction is needed between employees for learning because much of the knowledge of the process is tacit and so difficult to share in other ways. Offering these sharing possibilities can also impact positively to job satisfaction because they will increase the sense of community among employees which was found to be lacking. New employees demand more feedback to improve their learning and confidence. According to the literature continuous learning and job satisfaction have a relative strong relationship on productivity. The employee’s job description in the case organization has moved towards knowledge work due to continuous automation and expansion of the production process. This emphasizes the importance of continuous learning and means that productivity can be seen also from quality perspective. The normal productivity output in the case organization is stable and by focusing on the quality of work by improving continuous learning and job satisfaction the upsets in production can be handled and prevented more effectively. Continuous learning increases also the free human capital input and utilization of it and this can breed output increasing innovations that can increase productivity in long term. Also job satisfaction can increase productivity output in the end because employees will work more efficiently, not doing only the minimum tasks required. Satisfied employees are also found participating more in learning activities.

A multimethod examination of contributors to successful on-the-job learning for vocational students

Rapid changes in working life and competence requirements of different professions have increased interest in workplace learning. It is considered an effective way to learn and update professional skills by performing daily tasks in an authentic environment. Especially, ensuring a supply of skilled future workers is a crucial issue for firms facing tight competition and a shortage of competent employees due to the retirement of current professionals. In order to develop and make the most of workplace learning, it is important to focus on workplace learning environments and the individual characteristics of those participating in workplace learning. The literature has suggested various factors that influence adults' and professionals’ workplace learning of profession-related skills, but lacks empirical studies on contextual and individual-related factors that positively affect students' workplace learning. Workers with vocational education form a large group in modern firms. Therefore, elements of vocational students’ successful workplace learning during their studies, before starting their career paths, need to be examined. To fill this gap in the literature, this dissertation examines contributors to vocational students’ workplace learning in Finland, where students’ workplace learning is included in the vocational education and training system. The study is divided into two parts: the introduction, comprised of the overview of the relevant literature and the conclusion of the entire study, and five separate articles. Three of the articles utilize quantitative methods and two use qualitative methods to examine factors that contribute to vocational students’ workplace learning. The results show that, from the students’ perspective, attitudinal, motivational, and organizationrelated factors enhance the student’s development of professionalism during the on-the-job learning period. Specifically, the organization-related factors such as innovative climate, guidance, and interactions with seniors have a strong positive impact on the students’ perceived development of professional skills because, for example, the seniors’ guidance and provision of new viewpoints for the tasks helps the vocational students to gain autonomy at work performance. A multilevel analysis shows that of those factors enhancing workplace learning from the student perspective, innovative climate, knowledge transfer accuracy, and the students’ performance orientation were significantly related to the workplace instructors’ assessment regarding the students’ professional performance. Furthermore, support from senior colleagues and the students’ self-efficacy were both significantly associated with the formal grades measuring how well the students managed to learn necessary professional skills. In addition, the results suggest that the students’ on-the-job learning can be divided into three main phases, of which two require efforts from both the student and the on-the-job learning organization. The first phase includes the student’s application of basic professional skills, demonstration of potential in performing daily tasks, and orientation provided by the organization at the beginning of the on-the-job learning period. In the second phase, the student actively develops profession-related skills by performing daily tasks, thus learning a fluent working style while observing the seniors’ performance. The organization offers relevant tasks and follows the student’s development. The third level indicates a student who has reached the professional level described as a full occupation. The results suggest that constructing the vocational students’ successful on-the-job learning period requires feedback from seniors, opportunities to learn to manage entire work processes, self-efficacy on the part of the students, proactive behavior, and initiative in learning. The study contributes to research on workplace learning in three ways: firstly, it identifies the key individual- and organization-based factors that influence the vocational students’ successful on-the-job learning from their perspective and examines mutual relationships between these factors. Second, the study provides knowledge of how the factors related to the students’ view of successful workplace learning are associated with the workplace instructors’ perspective and the formal grades. Third, the present study finds elements needed to construct a successful on-the-job learning for the students.

Pharmacological differences between memory consolidation of habituation to an open field and inhibitory avoidance learning

Rats implanted bilaterally with cannulae in the CA1 region of the dorsal hippocampus or the entorhinal cortex were submitted to either a one-trial inhibitory avoidance task, or to 5 min of habituation to an open field. Immediately after training, they received intrahippocampal or intraentorhinal 0.5-µl infusions of saline, of a vehicle (2% dimethylsulfoxide in saline), of the glutamatergic N-methyl-D-aspartate (NMDA) receptor antagonist 2-amino-5-phosphono pentanoic acid (AP5), of the protein kinase A inhibitor Rp-cAMPs (0.5 µg/side), of the calcium-calmodulin protein kinase II inhibitor KN-62, of the dopaminergic D1 antagonist SCH23390, or of the mitogen-activated protein kinase kinase inhibitor PD098059. Animals were tested in each task 24 h after training. Intrahippocampal KN-62 was amnestic for habituation; none of the other treatments had any effect on the retention of this task. In contrast, all of them strongly affected memory of the avoidance task. Intrahippocampal Rp-cAMPs, KN-62 and AP5, and intraentorhinal Rp-cAMPs, KN-62, PD098059 and SCH23390 caused retrograde amnesia. In view of the known actions of the treatments used, the present findings point to important biochemical differences in memory consolidation processes of the two tasks.

Generalization of temporal order detection skill learning: two experimental studies of children with dyslexia

The objective of this study was to investigate the phenomenon of learning generalization of a specific skill of auditory temporal processing (temporal order detection) in children with dyslexia. The frequency order discrimination task was applied to children with dyslexia and its effect after training was analyzed in the same trained task and in a different task (duration order discrimination) involving the temporal order discrimination too. During study 1, one group of subjects with dyslexia (N = 12; mean age = 10.9 ± 1.4 years) was trained and compared to a group of untrained dyslexic children (N = 28; mean age = 10.4 ± 2.1 years). In study 2, the performance of a trained dyslexic group (N = 18; mean age = 10.1 ± 2.1 years) was compared at three different times: 2 months before training, at the beginning of training, and at the end of training. Training was carried out for 2 months using a computer program responsible for training frequency ordering skill. In study 1, the trained group showed significant improvement after training only for frequency ordering task compared to the untrained group (P < 0.001). In study 2, the children showed improvement in the last interval in both frequency ordering (P < 0.001) and duration ordering (P = 0.01) tasks. These results showed differences regarding the presence of learning generalization of temporal order detection, since there was generalization of learning in only one of the studies. The presence of methodological differences between the studies, as well as the relationship between trained task and evaluated tasks, are discussed.