Design document

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Design Documentation

Microsoft Academic Search Redesign Sponsor: Mihaela Vorvoreanu, Darrin Eide Advisor: Austin Toomb Present by Team Imperium Fall 2017

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Contents

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Research

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Project Overview

Microsoft Academic (MA) is a search engine for academic papers, unlike traditional keyword based search engine, MA applies a technique known as semantic inference to recognize the user’s intent and to proactively deliver results relevant to the user’s intention. As a result, MA can process complex queries and can provide rich and knowledgeable answers.

Working with a small team of researchers at Microsoft Research, our role is to evaluate v2.0 of Microsoft Academic Search (MAS) from a UX perspective.

Project Overview

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The first step of our project was establishing the goal and scope of the project. We first brainstormed the problem statements and three research questions, to then refine into a single one of each.

Problem statement (come up with three combinations) We must consider perspectives from both the experienced researcher and novice individuals. After thinking about their needs and wants we can tailor both our research question and problem statement to fit their personas.

Microsoft Research needs Purdue to gather and analyze user data in order to provide design recommendations for MAS 3.0.

MAS 2.0 is missing several UX Design principles and needs to be reevaluated to meet Microsoft’s design and usability standards for the MAS 3.0 release.

MAS 2.0 must undergo a massive overhaul that properly communicates to users with no technical, data science, or data visualization expertise on how advantageous their powerful semantic search is.

Research question (come up with three combinations) In order for the team at Purdue University to understand the users’ interactions with MAS, research was required to learn more about not only the users, but also semantic search engines and MAS itself.

How can we evaluate and gather user research data through testing that can inform the next version (v 3.0) of MAS? What needs to be changed to improve MAS 2.0?

Project Overview

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We want to evaluate and gather user research data through testing in order to better communicate to users of the tool how effective a semantic search like MAS could be.

How can MAS 2.0 be reevaluated through extensive research findings to better communicate an effective design for users as Microsoft begins to release MAS 3.0?

Problem statement: People lack familiarity with semantic search.

Research question: How can we develop a better understanding of semantic search through a more intuitive UI?

Through these two statements, we are able to establish our goals for this project.

Deliverables The project’s final deliverables are a list of design recommendations for MAS v3.0. These are recommendations based upon the project’s designs and testing of those designs. The design recommendations are communicated visually through a MVP prototype.

Project Overview

Methodology The research phase of each sprint acted differently than most user experience processes. Since the MAS project is divided into two teams, the majority of our work was solely focused on design.

Research We begin our process by undergoing research in order to better inform our design decisions as well as pinpoint usability errors and site strengths. In order to obtain this information we plan on using primary and secondary research methods to help fuel our ideations and lead us into the next phase which is design.

Design In this stage we begin turning concept into visual form. We begin with sketches, and slowly evolve it into a higher fidelity mockup. Lastly, we develop an interactive prototype that we can use to obtain more precise and accurate findings from our tests.

Testing The last phase is evaluation. In this stage we test our designs on our users to take note of the errors they are still having when operating the prototyped solution. We will then reiterate and conclude on a final design based on the feedback we were given.

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Research

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Research

Literature Review

We started with literature review. This would be an ongoing process where we collect publications, conference materials, and other documents to help us understand topics that are related around our project goal and could help with our design process.

Heuristic Analysis:

A quick internal evaluation of the website where the team identifies usability errors that violate Nielsen’s 10 Usability Heuristics.

Competitive Analysis A competitive analysis is used to evaluate how a given site’s competition compares against the usability standards and overall user experience of the site.

Research

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Literature Review This research, part of which being in the form of a literature review, was conducted with a library of 32 documents of various mediums media and sources, in various topics.

Of those 32 articles they could be further filtered into five categories. We first looked into semantic search to understand what it is and how it functions compared to keyword search. We then looked into keyword search to understand again the differences between how each system operates. Third, was Microsoft Academic search itself. We wanted to understand the system and how it embraced semantic knowledge. Fourthly, we looked at competitors in scholarly search. That was to understand the positives and negatives of their system and how we can design a product that can compete in the same market. Lastly we looked at Microsoft Fluent Design. As we started designing we needed a style and a theme to start restructuring the content. After suggestions from our sponsors at Microsoft we were told to look into Microsoft Fluent Design. Matching the same branding elements as most Microsoft UI’s will help keep the identity of the company.

A comprehensive list of all literature collections with it’s corresponding analysis can be found as a separate companion within the list of deliverables we handoff to the Microsoft team.

Some of the valuable quotes: “Those engines can only retrieve documents based on the containment of keywords or the document’s popularity instead of the documents real contents.”

“Only Google Scholar outperforms Microsoft Academic in terms of both publications and citations.”

“The Fluent Design System helps you create modern, clean UI that incorporates light, depth, motion, material, and scale.”

Research

Heuristic Analysis A heuristic analysis was also completed by both capstone teams. This was a quick and efficient way of internally evaluating the system and identifying usability errors that violate Nielsen's 10 heuristics which we chose to review the system with.

Visibility of system status - The system should always keep users informed about what is going on, through appropriate feedback within reasonable time.

Match between system and the real world - The system should speak the users’ language, with words, phrases and concepts familiar to the user, rather than system-oriented terms. Follow real-world conventions, making information appear in a natural and logical order.

User control and freedom - Users often choose system functions by mistake and will need a clearly marked “emergency exit” to leave the unwanted state without having to go through an extended dialogue. Supports undo and redo and a clear way to navigate.

Consistency and standards - Users should not have to wonder whether different words, situations, or actions mean the same thing. Follow platform conventions.

Error prevention - Even better than good error messages is a careful design, which prevents a problem from occurring in the first place.

Recognition rather than recall - Minimize the user’s memory load. Make objects, actions, and options visible. The user should not have to remember information from one part of the dialogue to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate.

Flexibility and efficiency of use - Accelerators -- unseen by the novice user -- may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent action.

Aesthetic and minimalist design - Dialogue should not contain information, which is irrelevant or rarely needed. Every extra unit of information in a dialogue competes with the relevant units of information and diminishes their relative visibility. Visual layout should respect the principles of contrast, repetition, alignment, and proximity.

Help users recognize, diagnose, and recover from errors - Error messages should be expressed in plain language (no codes), precisely indicate the problem, and constructively suggest a solution.

Help and documentation - Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user’s task, list concrete steps, to be carried out, and not be too large.

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