Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System

Abstract. Students entering a new field must learn to speak the specialized language of that field. Previous research using automated measures of word overlap has found that students who modify their language to align more closely to a tutor's language show larger overall learning gains. We present an alternative approach that assesses syntactic as well as lexical alignment in a corpus of human-computer tutorial dialogue. We found distinctive patterns differentiating high and low achieving students. Our high achievers were most likely to mimic their own earlier statements and rarely made mistakes when mimicking the tutor. Low achievers were less likely to reuse their own successful sentence structures, and were more likely to make mistakes when trying to mimic the tutor. We argue that certain types of mimicking should be encouraged in tutorial dialogue systems, an important future research direction

Designing Adaptive Instruction for Teams: a Meta-Analysis

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially-intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams

Modeling Language Characteristics of Leaders in Authoritarian Regimes over Decades

The present research investigated the linguistic patterns in the discourse of three prominent autocratic political leaders whose reigns lasted for multiple decades. The texts of Fidel Castro, Zedong Mao, and Hosni Mubarak were analyzed using computational linguistic methodologies and nonlinear modeling techniques to explore the temporal trajectory of formality over time. Specifically, this metric of formality increases with abstractness of words, syntactic complexity, cohesion (referential and deep), and the informational genre (as opposed to narrative). At the other end of the continuum, informal discourse tends to have concrete words, simple syntax, low cohesion and high narrativity. The findings are aligned with theoretically grounded hypotheses of aging and persuasion in hopes of identifying which most appropriately explains the formality of leaders’ political texts

How do we model learning at scale?:A systematic review of research on MOOCs

Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p

Emotions used in Learning Analytics: a state-of-the-art review

Emotions play a critical role in the learning and teaching process because learners’ feelings impact motivation, self-regulation and academic achievement. In this literature review of 100+ studies, we identify approximately 100 different emotions that may have a positive, negative or neutral impact on learners’ attitudes, behaviour and cognition. In this review, we explore seven methods of data gathering approaches to measure and understand emotions (i.e., content analysis, natural language processing, behavioural indicators, quantitative instruments, qualitative approaches, well-being word clouds, and intelligent tutoring systems). With increased affordances of technologies to continuously measure emotions (e.g., facial and voice expressions with tablets and smart phones), it might become feasible to monitor learners’ emotions on a real-time basis in the near future

Suicide vulnerability and risk: fragmented sense of self and psychache

Suicidality research has largely focused on psycho-social or demographic suicide risk factors, with less emphasis generally being directed towards understanding individual vulnerability factors. Moreover, although previous research has indicated that suicidality is underscored by the inner workings of personhood, suitable phenomenological approaches appear to have been infrequently applied. Accordingly, this thesis aimed to explore suicidal tendencies as predicted by low sense of self-cohesion, low self-esteem, psychological pain, distress, and emotions that underlie those psychological states. The selection of theoretically derived psychological factors was guided by the theories of Edwin S. Shneidman (psychache), and Heinz Kohut (self and self-cohesion). The cross-sectional nonexperimental design involved a survey of university students and staff, and volunteers from the South Australian community (N = 359, 72% females, 28% males, aged 18-67 years [M = 28.72; SD = 12.29]). Participants completed a battery of psychometric instruments, assessing self-cohesion, psychache, self-esteem, and psychological distress: 1) The Psychological Pain Assessment Scale (PPAS); 2) The Orbach and Mikulincer Mental Pain scale (OMMP); 3) The Psychache Scale (PS); 4) The Adelaide Self-Cohesion Scale (ASCS); 5) The Depression Anxiety Stress Scales 21 (DASS 21); 6) The Beck Self-Esteem scale (BSE); and 7) recent suicidality (from The Psychiatric Symptom Frequency Scale), lifetime attempts, and current suicidality indices. The thesis involved five studies, with results from each informing subsequent studies. Study 1 examined the psychometric properties of ASCS. Its three-factorial structure was replicated, confirming its validity for assessing a sense of self-cohesion. Study 2 tested relationships between self-cohesion, self-esteem, psychache, depression, anxiety, and stress. Exploratory factor analysis, followed by a Schmid-Leiman solution, found near-equivalence between the psychache measures OMMP and PS. Self-cohesion, self-esteem, psychache, depression, anxiety, and stress emerged as distinct but inter-related constructs, all strongly loading on a general factor of psychological frailty. Studies 3 and 4 explored relationships between these psychological factors and recent suicidal ideation/action, and history of suicide attempts. The utility of ASCS for assessing suicidality was also examined. The strongest contributor to recent suicidal ideation/action was depression, followed by self-esteem (part of self-cohesion) and psychache. The strongest contributor to lifelong suicide attempts was anxiety, followed by unmet childhood needs (part of self-cohesion). Study 5 clarified the nature of psychache in relation to three suicidality indices (recent suicidal ideation/action, lifetime attempts, and current suicidality). Two negative emotions underscored intense psychache across the three suicidality measures: self-hate and worthlessness. Additionally, “lure of death” was associated with lifetime suicide attempts only. Sadness, betrayal, and anger had negative associations with suicidality indices; hopelessness was associated with recent/current ideation, but not suicidal actions. It was concluded that suicide vulnerability is characterised by anxiety and a diminished sense of self, originating in early developmental frustrations resultant from unmet psychological needs. Further, tendencies for suicidal ideation/behaviours may partially be attributed to heightened levels of depression and psychache, and lowered self-esteem. As a clinical implication of the findings, it was proposed that a personal capacity for self-empathy may counter limitations of the self, help mollify deleterious effects of psychache and depression, reducing potential for suicide.Thesis (Combined MPsych (Clin) & Ph.D.) -- University of Adelaide, School of Psychology, 201

Use of recurrence quantification analysis to examine associations between changes in text structure across an expressive writing intervention and reductions in distress symptoms in women wth breast cancer

The current study presents an exploratory analysis of using Recurrence Quantification Analysis (RQA) to analyze text data from an Expressive Writing Intervention (EWI) for Danish women treated for Breast Cancer. The analyses are based on the analysis of essays from a subsample with the average age 54.6 years (SD = 9.0), who completed questionnaires for cancer-related distress (IES) and depression symptoms (BDI-SF). The results show a significant association between an increase in recurrent patterns of text structure from first to last writing session and a decrease in cancer-related distress at 3 months post-intervention. Furthermore, the change in structure from first to last essay displayed a moderate, but significant correlation with change in cancer-related distress from baseline to 9 months post-intervention. The results suggest that changes in recurrence patterns of text structure might be an indicator of cognitive restructuring that leads to amelioration of cancer-specific distress

Advancement Auto-Assessment of Students Knowledge States from Natural Language Input

Knowledge Assessment is a key element in adaptive instructional systems and in particular in Intelligent Tutoring Systems because fully adaptive tutoring presupposes accurate assessment. However, this is a challenging research problem as numerous factors affect students’ knowledge state estimation such as the difficulty level of the problem, time spent in solving the problem, etc. In this research work, we tackle this research problem from three perspectives: assessing the prior knowledge of students, assessing the natural language short and long students’ responses, and knowledge tracing.Prior knowledge assessment is an important component of knowledge assessment as it facilitates the adaptation of the instruction from the very beginning, i.e., when the student starts interacting with the (computer) tutor. Grouping students into groups with similar mental models and patterns of prior level of knowledge allows the system to select the right level of scaffolding for each group of students. While not adapting instruction to each individual learner, the advantage of adapting to groups of students based on a limited number of prior knowledge levels has the advantage of decreasing the authoring costs of the tutoring system. To achieve this goal of identifying or clustering students based on their prior knowledge, we have employed effective clustering algorithms. Automatically assessing open-ended student responses is another challenging aspect of knowledge assessment in ITSs. In dialogue-based ITSs, the main interaction between the learner and the system is natural language dialogue in which students freely respond to various system prompts or initiate dialogue moves in mixed-initiative dialogue systems. Assessing freely generated student responses in such contexts is challenging as students can express the same idea in different ways owing to different individual style preferences and varied individual cognitive abilities. To address this challenging task, we have proposed several novel deep learning models as they are capable to capture rich high-level semantic features of text. Knowledge tracing (KT) is an important type of knowledge assessment which consists of tracking students’ mastery of knowledge over time and predicting their future performances. Despite the state-of-the-art results of deep learning in this task, it has many limitations. For instance, most of the proposed methods ignore pertinent information (e.g., Prior knowledge) that can enhance the knowledge tracing capability and performance. Working toward this objective, we have proposed a generic deep learning framework that accounts for the engagement level of students, the difficulty of questions and the semantics of the questions and uses a novel times series model called Temporal Convolutional Network for future performance prediction. The advanced auto-assessment methods presented in this dissertation should enable better ways to estimate learner’s knowledge states and in turn the adaptive scaffolding those systems can provide which in turn should lead to more effective tutoring and better learning gains for students. Furthermore, the proposed method should enable more scalable development and deployment of ITSs across topics and domains for the benefit of all learners of all ages and backgrounds

Peer and Professor Relationship Quality: A Moderation Model for Student Persistence in Distance Education

Relationships matter, as hypothesized. This study finds the association between the DE relationship variables of COI relationships, teaching alliance, student bonding, professor rapport, and impact of face-to-face learning on DE psychology/counseling graduate students’ factors are significant in relationship bonding environment. The ubiquity of relationships is seen in educational and social growth contexts within various populations’ bonding environments. Our research further establishes that social contact alliances and interaction are necessary elements for effective DE in psychology/counselor education. The findings of this DE study is consistent with psychological research on the associated factors within therapeutic bonding relationships. Results address whether the direct effect of COI on the intention to persist was contingent in any way on intensive participation and TA/WA. Only NSSE-Professor was a significant predictor of intent to persist. This suggests that the relationship between professors and students may be a key element in students’ completion of an educational program