The use of mobile technologies is reshaping how we teach and learn. In this paper we describe our research on the use of these technologies to teach physics. On the one hand we develop mobile applications to complement the traditional learning and to help students learn anytime and anywhere. The use of this applications has proved to have very positive influence on the students engagement. On the other hand, we use smartphones as measurement devices in physics experiments. This opens the possibility of designing and developing low cost laboratories where expensive material can be substituted by smartphones. The smartphones' sensors are reliable and accurate enough to permit good measurements. However, as it's shown with some examples, here special care must be taken if one doesn't know how these apps used to access the sensors' data are programmed.

The use of mobile technologies is reshaping how we teach and learn. In this paper we describe our research on the use of these technologies to teach physics. On the one hand we develop mobile applications to complement the traditional learning and to help students learn anytime and anywhere. The use of this applications has proved to have very positive influence on the students engagement. On the other hand, we use smartphones as measurement devices in physics experiments. This opens the possibility of designing and developing low cost laboratories where expensive material can be substituted by smartphones. The smartphones' sensors are reliable and accurate enough to permit good measurements. However, as it's shown with some examples, here special care must be taken if one doesn't know how these apps used to access the sensors' data are programmed.

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Stop word identification is one of the most important tasks for many text processing applications such as information retrieval. Stop words occur too frequently in documents in a collection and do not contribute significantly to determining the context or information about the documents. These words are worthless as index terms and should be removed during indexing as well as before querying by an information retrieval system. In this paper, we propose an automatic aggregated methodology based on term frequency, normalized inverse document frequency and information model to extract the light stop words from Persian text. We define a ‘light stop word’ as a stop word that has few letters and is not a compound word. In the Persian language, a complete stop word list can be derived by combining the light stop words. The evaluation results, using a standard corpus, show a good percentage of coincidence between the Persian and English stop words and a significant improvement in the number of index terms. Specifically, the first 32 Persian light stop words have a great impact on the index size reduction and the set of stop words can reduce the number of index terms by about 27%.

Context has long been considered very useful to help the user assess the actual relevance of a document. In Web searching, context can help assess the relevance of a Web page by showing how the page is related to other pages in the same Web site, for example. Such information is very difficult to convey and visualise in a user friendly way. In this paper we present the design, implementation and evaluation of a graphical visualisation tool aimed at helping users to determine the relevance of a Web page by displaying the structure of the Web site the page belongs to. The results of an initial evaluation shows that this visualisation technique helps the user navigate large Web sites and find useful information in an effective way, without increasing the cognitive load of the user.