Toine Andernach

Bio: Toine Andernach is an academic researcher from University of Twente. The author has contributed to research in topics: Project management 2.0 & Educational technology. The author has an hindex of 5, co-authored 10 publications receiving 104 citations. Previous affiliations of Toine Andernach include Delft University of Technology.

Web Environments for Group-Based Project Work in Higher Education

Abstract: We discuss problems confronting the use of group-based project work as an instructional strategy in higher education and describe two courses in which course-specific World Wide Web (Web) environments have evolved over a series of course sequences and are used both as tool environments for group-process support and as the product environment of the project work itself. In particular we describe the use of specific Web-embedded shared workspace, communication-management and evaluation tools and their contribution to the management and educational value of group-based project work. The integration of instructional principles and strategies with the Web-based tools is also of particular importance. The 1996-97 versions of the courses analysed in this article can be found at http://www.to.utwente.nl/ism/ism1-96/home.htm for the first-year course in educational media design, and at www.edu.cs.utwente.nl/~aitnlpbg/, for the first-year course in applications of information technology. Both courses, at the University of Twente, use group-based project work as a major organizational form, but integrate all aspects of the courses within cohesive Web environments.

A Machine Learning Approach to the Classification of Dialogue Utterances

Abstract: The purpose of this paper is to present a method for automatic classification of dialogue utterances and the results of applying that method to a corpus Superficial features of a set of training utterances (which we will call cues) are taken as the basis for finding relevant utterance classes and for extracting rules for assigning these classes to new utterances Each cue is assumed to partially contribute to the communicative function of an utterance Instead of relying on subjective judgments for the tasks of finding classes and rules, we opt for using machine learning techniques to guarantee objectivity

The Use of Teaching Assistants in Project Based Learning at Aerospace Engineering

Abstract: Project-based learning by its nature requires a lot of staff efforts in terms of tutoring of the project groups. At the Faculty of Aerospace Engineering at Delft University of Technology in the Netherlands, traditionally in the first two years of the BSc degree use is made of senior and Master students to take over part of the tutoring role from the academic staff. This is due to the fact that in the Netherlands the number of PhD students and academic staff per 100 students is relatively low in comparison to the US. This paper describes how these assistants are used in the projects, what their responsibilities are, and the need for training. The pros and cons of using teaching-assistants in project-based learning are also discussed with a clear look at the ethical side of the use of teaching-assistants. Also a close look will be taken at the limitations of the use of teaching-assistants.

The Web as Process Tool and Product Environment for Group-Based Project Work in Higher Education.

Abstract: This paper discusses problems confronting the use of group-based project work as an instructional strategy in higher education, and describes two technical courses (i.e., courses in online learning and applications of business information technology) at the University of Twente (Netherlands) in which course-specific World Wide Web environments are used both as tools for group-process support and as the product environment of the project work. The focus is on Web-embedded shared workspace, communication management, evaluation tools, and their contribution to the management of group-based project work. It is argued the both pedagogical and technical strategies are needed for efficient and effective support of group-based project work in higher education. A table highlights particular strengths of the courses in relation to persistent problems in the educational deployment of group-based project work.

Language analysis for dialogue management in a theatre information and booking system

Abstract: SCHISMA is a joint research project of KPN (Royal PTT Nederland) and the University of Twente. The project aims at providing a natural language dialogue system which interfaces a database containing information about theatre performances in a certain city or region. The interface should make it possible to ask about performances in general, to tune in to a specific performance and, if desired, make a reservation for this performance. Research until now has concentrated on various aspects of realising such a theatre information and booking system. Among these aspects are the building of a Wizard of Oz environment for the acquisition of a corpus of dialogues for this domain, analysis and tagging of the dialogue corpus, recognition of domain-specific concepts (actors, authors, plays, dates, etc.), syntactic analysis and dialogue modelling. The emphasis in this paper will be on the analysis of individual utterances at various levels. Most important for the project are short and medium term goals of delivering prototype systems that allow demonstration of the system and evaluation of the design choices. Due to these goals the project does not strive at incorporating advanced but isolated research results on discourse models and syntactic and semantic analysis. Rather we investigate how to identify user preferences and how to embed systems like these in a more comprehensive environment of information and transaction services.

Management teams: Why they succeed or fail

Abstract: A Study of Teams: How It All Began The Apollo Syndrome Teams Containing Similar Personalities Identifying further Team Roles Team Leadership The Missing Team Roles Developing an inventory Unsuccessful teams Winning teams Ideal team size Features of good members of a team Teams in Public Affairs How Belbin reports developed Case Studies in Using Belbin

Computer-Supported Collaborative Learning in Higher Education.

Abstract: Computer-Supported Collaborative Learning in Higher Education provides a resource for researchers and practitioners in the area of computer-supported collaborative learning (also known as CSCL); particularly those working within a tertiary education environment. It includes articles of relevance to those interested in both theory and practice in this area. It answers such important current questions as: how can groups with shared goals work collaboratively using the new technologies? What problems can be expected, and what are the benefits? In what ways does online group work differ from face-to-face group work? And what implications are there for both educators and students seeking to work in this area?

Dialogue Act Tagging with Transformation-Based Learning

Abstract: For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.

Exploring collaborative online learning

Abstract: An investigation was carried out to determine the extent to which evidence of collaborative learning could be identified in students’ textual interactions in an online learning environment. The literature on collaborative learning has identified a range of behaviors that characterize successful collaborative learning in face-to-face situations. Evidence of these behaviors was sought in the messages that were posted by students as they interacted in online work groups. Analysis of students’ contributions reveals that there is substantial evidence of collaboration, but that there are differences between conventional face-to-face instances of collaborative learning and what occurs in an asynchronous, networked environment.

Dialogue Models for Inquiry and Transaction

Abstract: set of n-tuples description Vw(λ~x.φ) {w | Vw(λ~x.φ) = E} (E ⊆ Dn) question proposition partition answerhood {v | Vv(λ~x.φ) = Vw(λ~x.φ)} {{v | Vv(λ~x.φ) = Vw(λ~x.φ)} | w ∈ W} conditions Figure 3.4: Extension and intension of assertions, abstracts and questions (33) Does John love Mary? ?love(john,mary) extension at w: {v | Vv(love(john,mary)) = Vw(love(john,mary))} intension set: {{v | Vv(love(john,mary)) = Vw(love(john,mary))} | w ∈ W} The question relation of the question in example (34) is λx.love(john, x). The extension at w is the complete answer at w: the set of worlds that agree with w on the objects that satisfy the question relation. Each answer roughly corresponds to a subset E of the set of objects that are initially plausible answer candidates. The intension can again be derived by abstracting over w. Worlds that are indistinguishable with respect to their answers, are grouped together. Therefore a wh-question produces a partition in as many blocks, as there are subsets of the set of plausible answer candidates. (34) Whom does John love? ?x.love(john.x) extension at w: {v | Vv(λx.love(john, x)) = Vw(λx.love(john, x)) ) intension set: {{v | Vv(λx.love(john, x)) = Vw(λx.love(john, x))} | w ∈ W} Now we can motivate definition 8 and 9 of extension and intension for each type of expression in the language. The intuitions are summarised in figure 3.4. These notions can be expressed more elegantly in a two-sorted type-theory (Groenendijk and Stokhof 1989). For an assertion, the extension is a truth value that indicates whether the asserted is satisfied at the world of evaluation. The intension is a characteristic function: a function from worlds to truth values. In general, the intension of an expression is a function that maps worlds onto the extensions. Based on the intension, we can derive what one may call the intension set: the set of worlds that share a particular extension value. What value that is, depends on the success conditions of the particular way the expression is used. For an assertion, that value is typically the truth value ‘1’. For a term, the extension is its interpretation in a world: an object from the domain, as in definition 6. Its intension is a function from worlds to objects: an individual concept. We can again derive an intension set. A minimal requirement is that the term is defined in a particular world. So a natural intension set for a term would be the set of worlds for which the extension is defined. This corresponds to the presupposition of the term. An n-place abstract expresses some (question) relation. Its extension is analogous to the interpretation of a basic predicate symbol. It denotes the set of n-tuples of objects that satisfy the relation at a particular world. Again, we can define an intension set. Any set of applicable tuples E ⊆ Dn may count as the typical extension value of an n-place abstract. The set of worlds with non-empty answer sets corresponds to the conventional presupposition of the question corresponding to the n-place abstract. 3.3. UPDATE SEMANTICS WITH QUESTIONS 83 Finally, a question has as its extension in a world, the complete answer to the question at that world. Answers are propositions, modelled by a set of worlds. In this case, the set of worlds that agree on their extension of the n-place abstract. So the extension of a question equals the intension set of an abstract. The intension of an question is again the functin that maps worlds onto their extensions. The corresponding intension set is therefore a partition of the set of worlds where the question makes sense. Here is the definition of the extension of an expression relative to a particular context of evaluation: a model, a world and an assignment. Sequences of expressions φ;ψ are dealt with when we come to updates, in definition 15. Definition 8 (Extension) Given a model M, define for each φ ∈ L0 the extension at w under g Vw,g(φ) = { 1, if M,w, g |= φ 0, otherwise Vw,g(?~x.φ) = {v | Vv,g(λ~x.φ) = Vw,g(λ~x.φ)} where Vw,g(λ~x.φ) = {~ d ∈ Dn | M, g[~x 7→ ~ d],w |= φ} 2 Note that the definition for assertions is equal to that of abstracts for ~x = 〈〉 when you take {〈〉} as ‘1’ and ∅ as ‘0’. Also yes/no questions are an instance of the general case for questions. If the empty sequence 〈〉 makes the question relation true, this may be classified as a ‘yes’. If no sequence makes the question relation true, the answer was ‘no’. Proposition 1 (Yes/No Questions) For each model M, world w and assignment g Vw,g(?φ) = { {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = {〈〉}}, if M, g,w |= φ (’yes’) {v | Vv,g(λ〈〉.φ) = Vw,g(λ〈〉.φ) = ∅}, otherwise (’no’) Proof By definition. 2 The intension of an expression is the extension abstracted over the context. Given an assignment g, this produces for each φ a representative function 〈[φ]〉g from worlds to extensions. Definition 9 (Intension) Given model M and assignment g define for each φ ∈ L 〈[φ]〉g = λv.Vv,g(φ). 2 Based on the representative function we can derive an equivalence relation. Worlds are grouped according to their extensions. Using the earlier notation for equivalence under a given function we write '〈[φ]〉g . The notion of [[φ]]g, the set of worlds that satisfies a certain expression φ under g corresponds to what we called the intension set. It defines the content of an utterance. For assertives, [[φ]]g is called the proposition expressed by φ. For questions we can define a corresponding notion. [[?~x.φ]]g is the partition generated by '〈[?~x.φ]〉g . This partition structure is called the issue expressed by ?~x.φ. Definition 10 (Proposition; Issue) Given model M and assignment g, define for each φ ∈ L [[φ]]g = {w | Vw,g(φ) = 1} [[?~x.φ]]g = {{w ∈ W | Vw,g(?~x.φ) = Vv,g(?~x.φ)} | v ∈ W} 2 Incidently, this notion of content can be seen as a projection of the inverse function of the intension applied to the typical result of an utterance of that type, specified by the success 84 CHAPTER 3. INQUIRY conditions. The typical result of an assertion is the truth value 1; a denial has 0. The typical result of a question is a proposition corresponding to a so called nominal answer: a set of tuples of objects that possibly satisfy the question relation. A nominal answer E ⊆ Dn is called possible in case there is some world conceivable that has it as a complete answer. Impossible answers are those which are not supported by any world, for instance because they violate some meaning postulate or because some presuppositions in the question are not satisfied. Definition 11 (Projected Inverse) For any function f :: A → B define f−1 :: B → pow(A), the projected inverse of f , by f−1(b) = {a ∈ A | f (a) = b} (b ∈ B) 2 Proposition 2 (Inverse) For each φ ∈ L, its content is the projection of the inverse intension on a typical result: (i) [[φ]]g = 〈[φ]〉−1 g (1) (ii) [[?~x.φ]]g = { 〈[?~x.φ]〉−1 g {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } Proof (i) 〈[φ]〉−1 g (1) = {w | 〈[φ]〉g(w) = 1} (def) = {w | Vw,g(φ) = 1} (appl, def) = [[φ]]g (*) A nominal answer E ⊆ Dn to some question ?~x.φ is called possible, in case there is some v ∈ W such that Vv,g(λ~x.φ) = E. (ii) { 〈[?~x.φ]〉−1 g ({v | Vv(λ~x.φ) = E}) | E ⊆ Dn, E possible } = {{w | 〈[?~x.φ]〉g(w) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} | E ⊆ Dn, E possible } (appl, def) = {{w | Vw,g(?~x.φ) = {v | Vv(λ~x.φ) = E} = Vu,g(?~x.φ)} | u ∈ W} (def, *) = [[?~x.φ]]g 2 What to make of this result? For assertives, it highlights the special role played by the truth value ‘1’. For interrogatives, the function 〈[φ]〉−1 g applied to propositions that describe nominal answers is the identity function. This means that the two ways of generating a partition are essentially the same: grouping worlds that are indistinguishable with respect to an answer, or generating possible answers on the basis of a distribution of nominal answers (provided they are possible) and then finding the worlds to support them. So for the equivalence relation induced by a question, it does not matter whether you take indistinguishability with respect to the n-place abstract or with respect to the question: '〈[λx.φ]〉g = '〈[?x.φ]〉g. The distinction between extensions and intensions of questions is therefore less important for an update semantics with questions, where the equivalence relation is the central notion. 3.3.4 Information States Now we turn to a definition of the set of information states ΣW, on the basis of a given information space W. How are the separate data and structure aspects of an information state to be modelled? The factual information of an agent is modelled by a set of possible worlds: the worlds that are compatible with the information of the agent. This set is called the data set here. The semantic content of a question corresponds to a partition. This has 3.3. UPDATE SEMANTICS WITH QUESTIONS 85 been motivated above. We will use an equivalence relation to model the structure of issues. Partitions and equivalence relations are interchangeable. There is a choice. We can have the equivalence relation range over the complete information space, or over the particular data set of an agent. The choice only makes a difference from the external perspective. The first option makes most sense when issues are public: they are known to each participant on the basis of what was said earlier. Public issues may continue to play a role structuring the information space after they have been resolved for some particular agent. The second option makes most sense when it concerns private issues of an agent. Moreover, questions can contain referential expressions that are only defined in a certain subset of worlds. This context dependency is best modelled by relativising issues to a particular data set. Therefore we take the second option, to limit issues to a particular data set. The aspects of data and structure can now be combined into one equivalence relation σ over a subset of the set of all possible worlds W. From the equival