Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

Transforming a competency model to assessment items

The problem of comparing and matching different learners’ knowledge arises when assessment systems use a one-dimensional numerical value to represent “knowledge level”. Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multi-dimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner’s knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses the automatic generation of an assessment from the COMBA competency model as a “guideon- the–side”

Transforming a competency model to assessment items

The problem of comparing and matching different learners’ knowledge arises when assessment systems use a one-dimensional numerical value to represent “knowledge level”. Such assessment systems may measure inconsistently because they estimate this level differently and inadequately. The multi-dimensional competency model called COMpetence-Based learner knowledge for personalized Assessment (COMBA) is being developed to represent a learner’s knowledge in a multi-dimensional vector space. The heart of this model is to treat knowledge, not as possession, but as a contextualized space of capability either actual or potential. The paper discusses the automatic generation of an assessment from the COMBA competency model as a “guide-on-the–side”

On Verifying and Engineering the Well-gradedness of a Union-closed Family

Current techniques for generating a knowledge space, such as QUERY, guarantees that the resulting structure is closed under union, but not that it satisfies wellgradedness, which is one of the defining conditions for a learning space. We give necessary and sufficient conditions on the base of a union-closed set family that ensures that the family is well-graded. We consider two cases, depending on whether or not the family contains the empty set. We also provide algorithms for efficiently testing these conditions, and for augmenting a set family in a minimal way to one that satisfies these conditions.Comment: 15 page

An experiment on task performance forecasting based on the experience of different tasks

Performance in a task is influenced not only by the experience obtained in doing this task, but by how recent it is and by the experience obtained in doing similar tasks. Competence-Performance Approach is used as the theoretical framework. A modified version of Nembhard and Uzumeri learning and forgetting function is proposed to forecast performance by including the experience derived from other similar tasks. An experiment with voluntary students of telecommunication engineering was carried out. The tasks require assembly of electronic circuits. The results fitted well with the proposed model.Peer ReviewedPostprint (published version

Projections of a learning space

Any subset Q' of the domain Q of a learning space defines a projection of that learning space on Q' which is itself a learning space consistent with the original one. Moreover, such a construction defines a partition of Q having each of its classes defining a learning space also consistent with the original learning space. We give a direct proof of these facts which are instrumental in parsing large learning spaces.Comment: 13 pages, 1 figur

Multi-scenario modelling of learning

International audienceDesigning an educational scenario is a sensitive and challenging activity because it is the vector of learning. However, the designed scenario may not correspond to some learners’ characteristics (pace of work, cognitive styles, emotional factors, prerequisite knowledge, …). To personalize the learning task and adapt it gradually to each learner, several scenarios are needed. Adaptation and personalization are difficult because it is necessary on the one hand to know in advance the profiles and on the other hand to produce the multiple scenarios corresponding to these profiles. Our model allows to design many scenarios without knowing the learner profiles beforehand. Furthermore, it offers each learner opportunities to choose a scenario and to change it during their learning process. The model ensures that all announced objectives have enough resources for acquiring knowledge and activities for evaluation

Exploration of Student Online Learning Behavior and Academic Achievement

Students’ online persistence has typically been studied at the macro-level (e.g., completion of an online course, number of academic terms completed, etc.), and was investigated as a dependent variable with predicting variables such as motivation, engagement, economical support, etc. This study examines students’ persistence in an online adaptive learning environment called ALEKS, and the association between students’ academic achievement and persistence. With archived data that included students’ online math learning log and standardized tests scores, we first explored students’ learning behavior patterns with regard to how persistent they were while learning with ALEKS. Three variables indicating three levels of persistence were created and used for cluster analysis. Hierarchical clustering analysis identified three distinctive patterns of persistence-related learning behaviors: (1) High persistence and rare topic shifting; (2) Low persistence and frequent topic shifting; and (3) Moderate persistence and moderate topic shifting. We further explored the association between persistence and academic achievement. Analysis of covariance (ANCOVA) indicated no significant difference in academic achievement between students with different learning patterns. This result seems to suggest that “wheel-spinning” coexists with persistence and is not beneficial to learning. This finding also suggests that ALEKS, and other intelligent learning environments, would benefit from a mechanism that determines when a student fails that takes into account wheel-spinning behaviors. This would allow for a more appropriate intervention to be provided to learners in a timely manner