Adaptation and evaluation of a learning analytics dashboard to improve academic support at three Latin American universities     

Julio Guerra  Margarita Ortiz-Rojas  Miguel Angel Zúñiga-Prieto  Eliana Scheihing  Alberto Jiménez  Tom Broos  Tinne De Laet  Katrien Verbert 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(4):973-1001

Despite the success of academic advising dashboards in several higher educational institutions (HEI), these dashboards are still under-explored in Latin American HEI's. To close this gap, three different Latin American universities adapted an existing advising dashboard, originally deployed at the KU Leuven to their own context. In all three cases, the context was the main ruling factor to these adaptations. In this paper, we describe these adaptions using a framework that focuses on four different elements of the context: Objectives, Stakeholders, Key moment and Interactions. Evaluation of the adapted dashboards in the three different Latin American universities is conducted through pilots. This evaluation shows the value of the dashboard approach in different contexts in terms of satisfaction, usefulness and impact in academic decision-making and advising tasks. The main contribution of this paper is the systematic reporting of the adaptations to an academic advising dashboard and showing the value of an academic advising dashboard on academic decision-making and advising tasks.  相似文献   

Self-derivation through memory integration: A model for accumulation of semantic knowledge     

《Learning and Instruction》2020

Semantic knowledge accumulates through explicit means and productive processes (e.g., analogy). These means work in concert when information explicitly acquired in separate episodes is integrated, and the integrated representation is used to self-derive new knowledge. We tested whether (a) self-derivation through memory integration extends beyond general information to science content, (b) self-derived information is retained, and (c) details of explicit learning episodes are retained. Testing was in second-grade classrooms (children 7–9 years). Children self-derived new knowledge; performance did not differ for general knowledge (Experiment 1) and science curriculum facts (Experiment 2). In Experiment 1, children retained self-derived knowledge over one week. In Experiment 2, children remembered details of the learning episodes that gave rise to self-derived knowledge; performance suggests that memory integration is dependent on explicit prompts. The findings support nomination of self-derivation through memory integration as a model for accumulation of semantic knowledge and inform the processes involved.  相似文献   

Using semantic similarity to reduce wrong labels in distant supervision for relation extraction     

Chengsen Ru  Jintao Tang  Shasha Li  Songxian Xie  Ting Wang 《Information processing & management》2018,54(4):593-608

Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.  相似文献   

‘This could be our reality in the next five to ten years’: a blogpost platform as an expectation generation device on the future of insurance markets     

Gert Meyers  Ine Van Hoyweghen 《Journal of Cultural Economy》2018,11(2):125-140

William James (1919) characterises hypotheses as either live or dead. A hypothesis is live when it is taken into account as a ‘real possibility’. We follow James’ suggestion to not attribute intrinsic properties to hypotheses, but rather investigate how they came into being and look at the effects they generate. Expectations of digital technologies are a topic of vivid debate in the insurance industry. Before these expectations can become ‘live’, they have, in the first place, to be generated by market devices. We investigate how the reinsurance blogpost platform Open Minds functions as an ‘expectation generation device’ on the future of insurance markets. Combining Beckert’s work on the role of fictional expectations with the pragmatist turn in sociology of markets, we propose to study ‘expectation generation devices’, provoking expectations on economic markets. In our empirical analysis, we demonstrate the explicit fictional character of the Open Minds contributions, and analyse how a contained space of openness is generated to provoke expectations. We demonstrate how Open Minds can become live through circulation to other expectation generation sites in the insurance industry and beyond. We conclude by reflecting on the importance of expectation generation devices as a particular type of market devices.  相似文献   

The editorial playlist as container technology: on Spotify and the logistical role of digital music packages     

Maria Eriksson 《Journal of Cultural Economy》2020,13(4):415-427

ABSTRACT

This article explores the role of editorial playlists in Spotify’s streaming economy. In particular, it approaches Spotify’s playlists as container technologies – i.e. technical solutions that assemble, preserve, and transport music objects and thereby uphold logistical operations within the music industry. Such an approach seeks to complement previous research concerning playlists, which has often analyzed their emotional and affective dimensions but paid less attention to how playlists enhance calculative, mathematical, and logistical retail flows within the online music economy. On the one hand, the article considers how playlists – like containers in general – materialize principles of modularization and automation in ways that enhance control and remote oversight. On the other hand, it discusses how the playlist is far from a perfected means of measurement and control, and sometimes acts as an unruly transport device. Ultimately, the article shows how the playlist format occupies an uneasy position between order and disorder within the digital music economy which has not yet been fully accounted for in the context of music-oriented media studies.  相似文献   

LIS学术论文研究方法知识库构建与应用实验     

陈忆金 《图书馆论坛》2020,(6):47-54

为深入理解图书情报学学术论文中所使用的研究方法的语义功能,为用户提供基于知识单元的细粒度知识服务,文章首先基于体裁理论来分析引文分析法、田野研究法、共词分析法、实验法、比较分析法和问卷调查法的知识单元构成。其次采用文本分析法,检索CSSCI图书情报学领域的18种期刊中使用以上6种研究方法的论文,进行知识单元层次的深度标引,作为知识库构建的语料。再次采用系统设计法,开发具有4种功能的学术论文研究方法学习系统。最后采用实验法,招募30位研究生使用系统,并根据用户体验对其可用性进行评价。结果表明:研究方法的体裁分析能较好地表示使用该方法开展研究的论文的语义功能,解释研究方法使用过程各部分的语义特征,为深入到知识单元层面的标引提供了基础,也为用户提供了基于知识单元的细粒度的检索点,知识库具有很好的可用性。文章揭示了论文研究方法使用过程各部分的语义特征,基于知识单元构成而设计的学术论文研究方法知识库能有效帮助用户学习研究方法,为学术论文研究方法内容的深度语义标引和本体开发奠定了基础,也为用户提供细粒度、多维度的论文研究方法内容的检索服务,对面向知识发现的知识组织研究具有参考意义。  相似文献   

Neural embedding-based indices for semantic search     

Fatemeh Lashkari  Ebrahim Bagheri  Ali A. Ghorbani 《Information processing & management》2019,56(3):733-755

Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.  相似文献   

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis     

Kai Hu  Qing Luo  Kunlun Qi  Siluo Yang  Jin Mao  Xiaokang Fu  Jie Zheng  Huayi Wu  Ya Guo  Qibing Zhu 《Information processing & management》2019,56(4):1185-1203

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.  相似文献   

Rare-aware attention network for image–text matching     

《Information processing & management》2023,60(3):103280

Image and text matching bridges visual and textual modality differences and plays a considerable role in cross-modal retrieval. Much progress has been achieved through semantic representation and alignment. However, the distribution of multimedia data is severely unbalanced and contains many low-frequency occurrences, which are often ignored and cause performance degradation, i.e., the long-tail effect. In this work, we propose a novel rare-aware attention network (RAAN), which explores and exploits textual rare content for tackling the long-tail effect of image and text matching. Specifically, we first design a rare-aware mining module, which contains global prior information construction and rare fragment detector for modeling the characteristic of rare content. Then, the rare attention matching utilizes prior information as attention to guide the representation enhancement of rare content and introduces the rareness representation to strengthen the similarity calculation. Finally, we design prior information loss to optimize the model together with the triplet loss. We perform quantitative and qualitative experiments on two large-scale databases and achieve leading performance. In particular, we conduct 0-shot test for rare content and improve rSum by 21.0 and 41.5 on Flickr30K (155,000 image and text pairs) and MSCOCO (616,435 image and text pairs), demonstrating the effectiveness of the proposed method for the long-tail effect.  相似文献   

Are quiz-games an effective revision tool in Anatomical Sciences for Higher Education and what do students think of them?     

Kate Wilkinson  George Dafoulas  Hemda Garelick  Christian Huyck 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(3):761-777

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