Underpinning Theoretical Model for Data Graph Exploration

Based on the observations from the exploratory user study outlined above, we aimed to identify a suitable underpinning theoretical model to generate exploration paths for knowledge expansion in data graphs. Since our focus is knowledge expansion (i.e. assessing user learning), therefore we investigated two well-known learning theories, namely schema and subsumption theory for meaningful learning.

2.7.2.1 Schema Theory

Schema theory focuses on explaining how humans develop their cognitive structures (schemas) [139]. The underlying hypothesis of schema theory is that comprehension and learning of new concepts is based on relevant prior knowledge or schema [140]. This theory has been a major force in the development of reading models and had an important influence on research reading comprehension and learning [139]. The theory helped researchers and teachers to understand how knowledge is organised in memory and the role of an individual's prior knowledge in comprehension and learning while reading texts [139].

The term schema represents a cognitive structure that organises large amounts of information into a meaningful system [141]. A schema reflects a knowledge of the co- occurrence of elements, such as behaviours, features and objects that the individual has acquired through experience [142]. Rumelhart [143] highlighted several important characteristics of schemas: schemas represents knowledge rather than definitions, schemas represent knowledge at all levels of abstraction; and schemas are active (i.e. changeable) rather than being static.

According to schema theory, to learn new concepts (i.e. connect new concepts to existing schema), a person’s relevant schema is first activated (called schema activation) and then modified by bringing new concepts to it (schema modification) [144]. Schema activation is described as a continuous retrieval of relevant schemas from memory, and schema modification is an application of the activated schemas in new contexts or creation of new schemas [145]. According to [146] the process of schema modification includes three main learning tasks, namely accretion (when an existing schema from the prior knowledge is directly used to interpret a new concept), tuning (when an existing schema has to be slightly changed in order to understand a new concept), and restructuring (when an existing schema has to be significantly modified to create a new schema with new concepts).

2.7.2.2 Subsumption Theory for Meaningful Learning

David Ausubel, a Professor of Educational Psychology, aimed to help teachers to organise and convey learning materials to students in a meaningful way [147]. He argued that each of the academic disciplines has a structure of concepts, and there is a parallel between the way this structure is organised and the way humans organise their knowledge units in their existing cognitive structures [148].

Ausubels’ model of advance organisers served as a practical guide for assessing teachers in selecting, ordering and presenting new information to their students [25]. The advance organisers provide previews (usually in the form of written passages) at a higher level of abstraction than new learning materials, which are introduced to the students before bringing new concepts to. These organisers help the students to recognise what elements of new material can be meaningfully linked to relevant concepts in existing cognitive structure [26]. The underlying hypothesis of the advanced organiser model was that learning and retention of unfamiliar but meaningful (i.e. relevant to existing cognitive structures) learning material could be facilitated by the advance introduction of subsuming concepts [25].

The process of linking new learned material to pre-existing segments of knowledge in a cognitive structure is referred to as subsumption [149]. The subsumption theory for meaningful learning has been based on the premise that existing cognitive structure (i.e. individual’s organization, stability, and clarity of knowledge in a particular subject matter field) is the principal factor influencing the learning and retention of new material in a meaningful way. This theory for meaningful learning postulates that the human cognitive structure is hierarchically organised with respect to levels of abstraction and inclusiveness of concepts, where abstract and familiar concepts are deliberately introduced to the user prior to bringing new concepts to learn [25, 150]. The new concepts then become incorporated (anchored) under the relevant more abstract subsuming concepts in with insightful relationships, leading to meaningful learning [21, 25].

Ausubel in [150] argues that once the basic organising concepts (i.e. knowledge anchors) are identified33_{, attention can be directed towards identifying the presentation and} sequential arrangement of the new subsumed content (i.e. how to present the new learning material and in what order to the learner). With this regard, Ausubel hypothesised two principles progressive differentiation and integration reconciliation. One the one hand, the progressive differentiation postulates that an individual organisation of the content of a

33 According to Ausubel, subject matter experts and talented teachers are able to identify the basic organising concepts (i.e. knowledge anchors) in the subject field

particular domain consists of a hierarchical structure in which the most inclusive concepts exist at the most abstract level (apex) of the and subsume progressively less inclusive concepts. Ausubel described this process as a sphere of knowledge from regions of greater to lesser inclusiveness, each linked to the next higher step in the hierarchy through subsumption [25]. On the other hand, the integration reconciliation principle indicates that subsuming concepts explicitly indicate in what ways previously learned, related concepts in cognitive structure are either basically similar to or essentially different from the new concept.

Ausubel distinguished between four processes for meaningful learning [26], namely derivative subsumption, correlative subsumption and superordinate learning and combinatorial learning. Derivative subsumption occurs when the new learned material is an instance or example of an existing concept in the human cognitive structure (e.g. a person who learned that Vietnamese Guitar is an example of Guitar). Correlative subsumption occurs when the new leaned material is a modification or elaboration of existing concepts (e.g. a person sees Acoustic Guitar with 12 strings. This will alter the person’s knowledge about Guitar to include the possibility that a Guitar may have 12 strings). Superordinate learning occurs when an individual learn that learned concepts (e.g. Apple, Orange) may all be subsumed under the new term Fruit. Finally, combinatorial learning occurs when new material can’t be subsumed through a subordinate relationship nor a superordinate relationship to a particular relevant idea.

2.7.2.3 Underpinning Theoretical Model Identified

Schema theory (as outlined above) provides general description of how human cognitive structures are organised and developed. Whereas, the subsumption theory for meaningful learning provides more detailed description of how knowledge anchors in a data graph can be used to introduce and lean new concepts. Furthermore, Ausubel describes two types of meaningful relationships used in the first three processes for meaningful learning (outlined above), namely superordinate and subordinate relationships. We argue that these relationships can be aligned with the subsumption relationships (e.g. rdfs:subClassOf) in the context of a data graph. In other words, the subsuming entities (i.e. the knowledge anchors) and the subsumable entities (e.g. subclasses of the knowledge anchors) represent class entities in the data graph that are linked via subsumption relationship rdfs:subClassOf. Accordingly, the subsumption process can be applied in the context of a data graph. Therefore, our work will adopt the subsumption theory for meaningful learning as the underpinning theoretical model for generating exploration paths in data graphs to facilitate knowledge expansion. The theory will be applied over the two data graph (MusicPinta and L4All) for generating exploration paths in Chapter 7.